Mark Huckvale
Phonetics and
Linguistics
University College
London
April 2003
Computers and robots in science fiction books and films seem to engage effortlessly in conversations with their human masters. They understand what is asked of them and their replies make sense. But does science fiction give us a realistic impression of what it would be like to converse with a machine? What aspects of robot language are not only beyond current technology but also beyond common sense? How close is current technology to the linguistic skills of HAL or C3PO? This essay critically analyses examples drawn from science fiction to expose the real problems of building a conversational machine. Is the gap between fictional systems and current technology so large that we'll never be able to program a computer to understand human language?
Winston thought for a moment, then pulled the speak-write towards him and began dictating in Big Brother’s familiar style: a style at once military and pedantic, and, because of a trick of asking questions and then promptly answering them [..], easy to imitate.
George Orwell, Nineteen Eighty-Four, 1949.
Orwell’s “speak-write”
is what we now know as a dictation system, a mechanism for converting spoken
words to printed text without the need for a keyboard. The dictation systems of today have much the
capability expressed in Orwell's dystopian vision, and in fact are based on a
famous paper published in 1983! A
modern dictation system is not though, as Orwell imagined, a specialised
hardware device but packaged software that runs on a general-purpose digital
computer. Such a program transcribes what
you dictate into a sequence of words entered into a word processor, it allows
you to add punctuation and set formatting, and even checks your spelling and
grammar. Dictations systems constitute,
as we shall see, one of the few success stories of speech and language
technology.
A modern dictation
system has a very large vocabulary and allows you to dictate in short,
continuously-spoken phrases; it also understands editing commands so that it is
possible to create, edit, print and save a document purely by voice. Such systems usually require an enrolment
session where you must first read out a few hundred sentences so that your
pronunciation can be modelled. Systems
also benefit from a study of your existing documents to analyse your vocabulary
usage and writing style. In operation,
dictation systems learn from their mistakes and so require you to correct them
as you work. The performance of such a
system depends in part on how well you learn to modify your speaking style to
the expectations of the system. A
dictation system prefers consistent articulation of speech, with only small
variations in speaking rate or voice quality.
Well articulated speech is not necessarily easier for the system to
understand, because it is further from the average speaking style it was
trained on in the factory. Also
performance is strongly affected by how well the words spoken match the
system’s internal statistical model of word occurrence; unusual word sequences
are more likely to be mis-recognised. A
motivated, co-operative user can obtain less than 5% word error on the first
attempt at transcription of a sentence, and even after correction can obtain a
word transcription speed greater than most people can type.
What are the
weaknesses of this technology? Firstly,
these systems are optimised for the specific task of converting short phrases
into transcribed words. Their
specialisations include the use of a document/editing language model, their
requirement for on-line correction, and the way in which the dictation process
requires visual feedback. Removing any
of these will significantly affect performance. Just removing the facility for users to report recognition errors
will prevent the system learning from its mistakes and lead to an inability to
track changes over time in either the speaker’s voice or the content of
documents. However the most significant
problem with this technology is that at no time does the system have any
understanding of what the text being transcribed is actually about. This lack of awareness of topic or reference
often leads to bizarre interpretations of utterances when recognition
fails. You might produce a sentence
which seems clearly articulated, comprehensible and contextually relevant, but
the system types a word string containing the most odd and irrelevant
vocabulary. A human secretary would not
do this; it is more likely that he or she would ask for clarification or
substitute an alternative sentence that does make sense. Neither of these possibilities is currently
available to dictation system programs.
The lack of an ability to comprehend the text being dictated causes
other problems. Whereas a human
secretary can be told to “go back to the part where I was talking about
previous projects and insert a standard paragraph about the company history”, a
dictation system has no model of the content of the document above the level of
the words in the text. It cannot locate
a paragraph on the basis of what it is about; it cannot make changes to the
text based on its meaning.
[Gay Deceiver is a robot vehicle programmed by voice]
".. Hello Gay."
"Hello, Deety!"
"Analyze latest program execute-coded Gay Deceiver take us home. Report."
"Analysis complete."
"Invert analysis."
"Null program."
Deety sighed. "Typing a program is easier. New program."
"Waiting Deety."
"Execute-code new-permanent program. Gay Deceiver, countermarch! At new execute-code, repeat reversed in real time latest sequence inertials transitions translations rotations before last use of program execute-code Gay Deceiver take us home."
"New permanent program accepted."
Robert Heinlein, The Number of the Beast, Fawcett Columbine,
1980.
It is not surprising
that in fiction, computers recognise what people say without error. It would be tremendously dull to read about
someone battling to get a computer to recognise his voice. Likewise a computer so stupid that it
mis-recognised commands would not make a convincing antagonist in a movie
plot. However in reality, computers
mis-recognise speech all the time.
Despite enormous engineering efforts in signal processing and pattern
recognition, current speech recognition systems are often ten-times
worse than humans at recognising what somebody is saying. The advantage of dictation systems is that
they are tuned to one person’s voice, one style of speaking, one accent, one
person’s language use, one quiet environment.
But relax any of those assumptions and recognition gets worse: if the
system doesn’t know who is speaking, if the speaker changes his/her style of
speaking, if the speaker has a different accent to the one the computer was
trained on, if the topic of conversation changes, if the speech is produced
under varying background noise conditions.
A speech recognition algorithm that has perhaps 5% word error on the read
speech of a known speaker in a quiet place suddenly generates 50% word error on
spontaneous speech of an unknown speaker over a telephone. It is difficult to say what are the exact
causes of the weakness in current recognition approaches. It is likely that it is a combination of a
number of assumptions and approximations in the current models. For example, current systems make
assumptions about the ways in which the pronunciation of a word can vary which
are probably not correct; they also use a probabilistic model of the sounds of
speech that appears overly simplistic.
My own prejudices are that current systems have too much ‘built-in’
knowledge of the problem: constraints and structures imposed by their designers. Systems would be better if they were more
free to develop their own internal representations of the problem.
INTERIOR: REBEL BLOCKADE RUNNER -- SUBHALLWAY.
Artoo stops before the small hatch of an emergency lifepod. He snaps the seal on the main latch and a red warning light begins to flash. The stubby astro-robot works his way into the cramped four-man pod.
C3PO: Hey, you're not permitted in there. It's restricted. You'll be deactivated for sure.
Artoo beeps something to him.
C3PO: Don't call me a mindless philosopher, you overweight glob of grease! Now come out before somebody sees you.
Artoo whistles something at his reluctant friend regarding the mission he is about to perform.
C3PO: Secret mission? What plans? What are you talking about? I'm not getting in there!
Artoo isn't happy with Threepio's stubbornness, and he beeps and twangs angrily.
A new explosion, this time very close, sends dust and debris through the narrow subhallway. Flames lick at Threepio and, after a flurry of electronic swearing from Artoo, the lanky robot jumps into the lifepod.
C3PO: I'm going to regret this.
George Lucas, STAR WARS Episode IV A NEW HOPE, 1976
In the film Star Wars,
two of the main characters are robots.
R2D2 is a technical maintenance robot that is shaped like a squat
cylinder, while C3PO is a ‘protocol droid’ with a humanoid shape. R2D2’s forte is dealing with electronic and
mechanical machinery, and although it can understand human speech it can only
speak in beeps and whistles. C3PO is
designed for working with humans and can both perceive and produce human speech
as well as “over six million forms of communication”.
C3PO was voiced by the
actor Anthony Daniels with no noticeable electronic manipulation to make his
speech more mechanical. Daniel’s C3PO
has a style of speaking that makes him seem rather scatty and nervous, but it
is fundamentally a human voice.
C3PO’s use of spoken human language is, of course, essential to the
plot. If C3PO communicated to R2D2 in
beeps and whistles, we wouldn’t know about the “secret mission” in the scene
above. But to be intelligible to us,
the movie audience, C3PO has to communicate with a series of sounds that
simulate the sounds humans make with their vocal tracts. And in particular the sound coding system
belonging to English. The more you
think about this, the more ridiculous it appears. Here are two robots communicating in a mixture of two languages,
one of which is copied from the language used by a species of humans thousands
of years distant in another galaxy!
Neither robot has a vocal tract nor the limitations of human hearing;
why use the complexity and ambiguity of human speech when the information they
need to convey to each other would be better encoded, encrypted and sent by
wireless network?
My computer rarely
communicates to me in speech; in fact it rarely communicates with me in human
language at all. The English I see in
programs and messages are just stored character strings that are neither
generated nor interpreted by the computer.
The nearest my computer gets to communicating with me in English is when
it corrects my spelling and suggests changes to my use of English grammar. At least here its output is prompted by a
linguistic level of analysis of my interactions. Most computers communicate visually through graphics (windows,
cursors, buttons, icons) or through lists and tables (folders, menus). It would not necessarily be more convenient
for it to use speech, unless I was visually impaired. The utility of speech is in fact limited to certain types of
communication situation, and really only when one of the participants is human.
However, if a computer
system chooses to use the speech modality to communicate, then it must use that
modality in an explicit emulation of how humans use that modality. If it were to use a different sound code or
language, then the human would first have to learn the language of the machine;
and if the system used microwaves or ultrasound, the human couldn’t perceive it
at all. A robot doesn’t necessarily
have a vocal tract, but it must make sounds as if they had been
generated by a vocal tract because human speech perception is tuned to perceive
sounds generated that way. The
particular sounds we use to encode the identity of a word in speech are not
arbitrary but a collection of sounds that we can make with our own vocal
apparatus. Most languages of the world
have sounds like /b/ and /n/ and /i/ because they are easy to produce and
relatively distinct. The precise form
of the sounds we use in speech is strongly influenced by our specific anatomy
and neurophysiology. Our nearest animal
cousins, chimpanzees, have a similar vocal tract to us but are unable to
control it in a way that allows them to make speech-like vocalisations, even as
mimicry. But not only is the acoustic
form of each individual sound affected by the form of the human vocal tract, in
connected utterances the way in which one sound merges into the next depends
upon the dynamics of the articulators in the vocal tract: the rate at which the
tongue, jaw, lips and soft palate can move, and their tendency to undershoot
their targets. This kind of variation
in the acoustic form of speech sounds is not a problem for our perceptual
system because we expect that variation to occur. That is we expect that speech will be affected by limitations in
the speed and precision of articulations.
But a robot without a vocal tract is not subject to these limitations –
it has much more freedom in the range and dynamics of sounds. The problem is that this freedom makes the
speech sound "not human" and actually harder to process. To talk like
a human, then, means that our robot will need to have the limitations of
a human vocal tract.
You might think of
three ways in which a robot could obtain the use of a human vocal tract: it
might be provided with a hardware mechanism which copied the shape and
operation of a human vocal tract; it might be provided with a software
simulation of a human vocal tract; or it might use a model of the acoustics of
a human vocal tract. This last way
allows the computer to make speech-like sounds but not in a human-like
way. All of these have been tried, but
none produce connected speech that sounds as if it could have been produced by
a human being. The problem is that
there are inadequacies in the knowledge we have of vocal tract use and its
acoustic properties, as well as computational problems in simulating its
aerodynamics. Fortunately there is a
fourth approach, which has only recently come to prominence, and that is for
the robot to use a real human vocal tract!
Surprisingly, the most successful modern systems just use recordings of
actual human voices. These recordings
are indexed, cut-up and glued together to generate new utterances. The use of real recordings ensures that the
voice has fairly natural characteristics even though the assembly process adds
a somewhat unnatural prosody. This is
currently the best way for a computer to obtain use of a human vocal tract to
speak. It reminds us that in fact C3PO
only speaks through Anthony Daniel’s vocal tract.
The first generation of computers had received their inputs through glorified typewriter keyboards, and had replied through high-speed printers and visual displays. HAL could do this when necessary, but most of his communications with his shipmates was by means of the spoken word. Poole and Bowman could talk to HAL as if he were a human being, and he would reply in the perfect, idiomatic English he had learned during the fleeting weeks of his electronic childhood.
Arthur C Clarke, 2001: A Space Odyssey, 1968
But should a computer
sound exactly like a human being anyway? While it may be true that natural human speech is optimal in
terms of the ease with which humans can process it, that is not to say that
approximations to human speech cannot be made quite intelligible. In particular, models of the acoustics of
vocal tracts (so called formant synthesizers) can produce highly intelligible
speech although they often have a machine like character.
There is a real
problem with giving a robot a human-like voice, which is that we then make all
kinds of assumptions about its mental capabilities. A machine that sounds like a human leads us to believe that we
are listening to a conscious entity that has its own thoughts, desires,
intentions, emotions and so on. With
HAL this is exactly what we are meant to infer. HAL’s rather flat, cold creepy voice is part of the structure of
the film: it leads us into believing that a computer can become mentally
unstable and ultimately a killer. This
inference about the capabilities of the machine is dangerous because machines
will not have the same outlook on the world as humans. They won’t have the same drives, the same
understanding. They may not have
emotions. It seems pretty unlikely that
they will be conscious of their behaviour in exactly the same way that humans
seem to be. Anyway, if they were
conscious why would they want to speak with the voice of a different species
beyond a minimal requirement of intelligibility?
Another peculiar
aspect of robot speech is that the changes to the voice and style of speaking
used by a machine to convey emotion have also been copied from humans. But the changes in human voices that reflect
boredom, fear, tiredness or elation are surely caused by the cognitive and physiological
state of the person. Even a machine
with emotions (whatever that means) won't have human-like physiological
reaction to those emotions, and thus has no reason to simulate the effect of
human physiology on its speech unless it is trying to manipulate the emotions
of human listeners. Similarly, the use
of a harsh and monotonous robot voice quality in SF films is for our benefit,
not the robot's. An emotionless robot
needn't have a flat pitch or an over-regular rhythm; speech from a software synthesizer
needn't sound as if it came through a pipe or suffered amplitude
distortion. In an SF film, the use of
human emotional cues by machines is just a simple way in which the mental state
of the machine can be communicated to the audience. But if the machine actually was unstable, or had evil intentions,
there is no reason why this should affect its speech in such a way. The use of human voice attributes is
actually more confusing – it leads us to incorrect inferences about the mental
state of the machine. Of course we
don’t yet know what an authentic voice of a machine would be like. Until then we will continue to apply the
same judgements to a machine voice as we do to human voices; and we will
continue to make invalid inferences about its intelligence, understanding,
motivation and emotional state.
When C3PO or HAL talk,
they do so with perfectly judged expressive speech, with fluent articulation
and natural prosody. They are not
“reading a script” like an automated station announcement system. They appear to understand what they
say. Their communication has specific
goals. They appear to want those goals
to be achieved. However it is only
recent research that has studied how underlying concepts can be mapped onto
expressive speech. Most contemporary
speech output systems simply convert text to speech.
Modern text-to-speech
systems are able to produce highly-intelligible speech automatically from input
text. A typical system will process
input text through a number of stages: (i) pre-processing to convert
abbreviations, acronyms and numbers into words, (ii) phrasing to split the text
up into prosodic phrases, (iii) intonation marking to identify accented words,
(iv) pronunciation of words, (v) timing of phonetic elements, and (vi) signal generation.
Depending on the signal generation method, the output speech can have a robotic
or a human voice quality. However, in
either case, the output speech is still recognisably that of a machine since it
often has an unnatural rhythm and intonation.
Such systems also tend to operate poorly on highly structured material
such as tables, lists or formulae. The
speech output of text to speech systems sounds like the read speech of someone
that doesn’t understand the material.
The difficulty of
assigning a suitable rhythm and intonation in text to speech conversation is
mainly due to the fact that such assignment relies on more knowledge about the
text than is present in the text itself.
The prosodic properties of a sentence relate to the overall meaning and
function of the sentence, and this can’t be deduced by processing each word in
the sentence in isolation. The word
‘toe’ in the phrase “the toe nails on the other hand” must carry the main
accent otherwise the phrase doesn’t make sense. A system cannot get the prosody of such a sentence correct
without understanding its meaning.
Notice that the meaning and function of the sentence is not available to
the TTS system because the sentence was not generated by the system itself in
order to communicate. As we shall
discuss below, the extraction of meaning from text is far from understood. The very information the system needs to
generate adequate prosody is denied it when it reads from text.
Both C3PO and HAL have
the advantage that when they speak they have ‘made up’ the sentences
themselves. They have access to the
reasons why each sentence needs to be generated and what it means. They know for example which information is
old, which is new, which is important, which contradicts previous knowledge. This allows them to signal through prosody
the information structure of the sentence, to put focus on the most important
elements. Once again, however, the
rules they use about how to signal this structure can only be copied
from the rules used by humans to do the same task, otherwise the human
listeners would not understand the result.
HAL: I’m sorry Frank. I think you missed it. Queen to bishop 3, bishop takes queen, knight takes bishop, mate.
[..]
HAL: We can certainly afford to be out of communication for the short time it will take to replace it.
[..]
HAL: I honestly think you ought to sit down calmly, take a stress pill, and think things over.
Arthur C Clarke and Stanley Kubrik, 2001: A Space Odyssey
(Screenplay), 1968.
It is easy to forget
that computers and robots in SF films are actually conversing in what is, to
them, an alien language. Not only can
they recognise what people say and can generate speech that we can recognise,
they can analyse the meaning of sentences, and can generate sentences that
convey specific meanings as well as humans.
But how the meaning of utterances relates to the actual sequence of
words used is far from simple. The
syntax and semantics of natural languages has been studied for decades without
producing a definitive set of computational rules that map words to meanings or
vice versa. Consider the three
sentences spoken by HAL, above. Each of
these sentences uses the verb take and in each of them take has a
different meaning. In the first sentence
“knight takes bishop”, take means capture or get possession of. In the second sentence “time it will take”, take
refers to a defined period of time over which something is performed. In the third sentence “take a stress pill”, take
refers to ingestion. The word take
has a large number of other possible meanings.
My Webster’s dictionary lists over 50 related senses. Thus knowing that a sentence contains take
is not enough; a machine also needs to be able to determine which meaning of take
is being used. Word ambiguity is a
significant problem in finding the meaning of a sentence. Each ambiguous word in an utterance leads to
a factorial increase in the number of possible meanings of the utterance as a
whole. And furthermore, it is not the
case that the machine can choose between them on the basis of sense or
likelihood alone. The resolution of
ambiguity actually requires knowledge about the world – knowledge of what the
words in the utterance relate to.
A commonly quoted
example of these two issues is shown by the sentence “time flies like an
arrow”, which has been shown to have a hundred or more possible meanings! The difficulties arise because of the many
possible individual meanings of the words.
The word ‘time’ can be a noun (the thing time), an adjective (a type of
fly) or a verb (a command to measure duration). The word ‘flies’ can be a noun (some insects or parts of men’s
trousers) or a verb (movement through the air). The word ‘like’ can mean ‘similar-to’ or it can mean ‘fondness
for’. So how are we able to decide that
the reading of this utterance should be that “the nature of time is that it
travels rapidly in a straight line” rather than “there are certain kinds of
flying insects that demonstrate a fondness for arrows”, or any one of the many
other possible meanings? The knowledge
needed to pick the most favoured interpretation is not to be found in the
utterance itself. The ability to
choose between interpretations comes from an understanding we have about
objects in the world; our understanding of time, flying and arrows. But this understanding has to be deeper than
a logical statement about the semantics of individual words. Consider the two sentences “I saw the man
with the telescope” and “I saw the man with the apple”. Even ignoring the multiple meanings of
“saw”, both sentences are ambiguous with respect to whether phrase "with
X" refers to the man or the act of seeing. Was the man carrying a telescope/apple or did I see the man using
a telescope/apple? To choose the right
interpretation in each case requires more knowledge than what a telescope is or
what an apple is, it requires knowledge of what telescopes can be used for and
what apples can be used for. The
sentences themselves do not help us; we need to know facts about the world that
these sentences describe.
Giving a computer or
robot knowledge about the meaning of words so that they can analyse the meaning
of sentences is itself fraught with difficulty. I'll describe two particular classes of problem: grounding and
human perspective.
A dictionary defines
words in terms of their relations to other words. Thus from a dictionary it is possible to create a kind of web of
relationships, which for example relate seeing to eyes to perception to knowledge;
or which relate apples to vegetables to food.
Similarly a corpus of language describes likely combinations of words in
sentences, and the collocations of ideas expressed in those sentences might
tell you more about the ways in which the words are used. A corpus might tell you that seeing and
telescopes collocate more frequently than seeing and apples. Thus there seems some hope for linguistic
analysis to lead to a resolution of the ambiguity in "I saw the man with
the apple". But in general, such
types of linguistic analysis do not add to the knowledge of the system about
the world. They simply add to knowledge
about the statistics of words. The
words themselves remain abstract symbols that do not relate to real objects or
events in the real world. This is known
as the "symbol-grounding problem", and it comes about because we are
trying to learn about language from observation rather than by taking part in
linguistic exchange. The only way in
which a robot can know what "apple" refers to is if that robot has
also developed a concept of an apple and made the link between the spoken name
and the concept. Thus it appears that
the only way for a machine to use language as humans do would be for the
machine to learn language itself, not be given large quantities of digested
text.
But even if a robot
could learn language, this is not to say that its interpretation of language
would match that of a human being. The
problem is that so much human language is built from a human perspective of the
world. When we use "I see" to
mean "I know" we are just reflecting how much of our understanding of
the world comes through our visual sense.
The words we use for colours, sounds, tastes, smells and textures are
affected by our peculiar limitations in seeing, hearing, tasting, smelling and
touching. Perhaps even the way in which
we catalogue and categorise our experiences is affected by our human
limitations of perception and memory.
The complexity of the ideas we can comprehend, or the complexity of the
sentences we can understand may be limited by the size of working memory. Understanding language may require us to
understand the motivations and desires of other human beings. All these things suggest that to really
understand human language the way that humans do requires a machine to have a
knowledge of the human perspective.
We are a very long way
from programming systems of such complexity.
However progress has been made on understanding human language within
limited domains. For example, there have
been numerous attempts at building natural language interfaces to commercial
computer database products. Modern
databases store information in cross-linked tables and use a query language
called SQL to let users to locate information (rows, columns, cells, summaries)
according to supplied criteria. Since
SQL has an artificial syntax, many users find it difficult to learn. A tool that could convert everyday questions
into SQL could thus make the information in the database available to untrained
users. Thus a user may ask: “What
percentage of American customers bought an electrical product?” The problem in making such a translator is
that it must relate the meanings of words in the question to the information
present in the database. Not only must
the translator realise that an American customer means “customer-country=USA”,
and that calculating a percentage involves finding both a count and a total,
the translator must also know that customers are the kind of object that buy
products. On the whole these systems
are a collection of special tricks for dealing with particular phrases (“What
percentage …”) with techniques for parsing noun phrases using information drawn
from the database definition. Users
find such systems very valuable, but tellingly adjust the way in which they
construct natural language queries to the capabilities of the system. The users rapidly learn to avoid
constructions the system is unable to process correctly. Systems can also feed back to the user
information about the translation into SQL, either as SQL directly or as a
natural language paraphrase. This helps
the user learn what range of questions he is able to ask.
The weakness of such
technology for building a conversational machine is the fact that the system
only attempts to understand questions about a given database. The system searches for an interpretation of
the question under the assumption that it is about the information in a
database. Although it is able to
present its translation as a formal expression in SQL, it does not of course have
an understanding of any of the symbols it uses. It may know that a particular column expresses the colour of a
product, but it has no knowledge of what a colour is. This means it is unable to reflect on the meaning of any of the
attributes in the database: “which product versions have a reddish
colour?” Another problem is that such a
system does not scale to larger applications.
As the number of tables and field names and types increase then the
potential for ambiguity also increases.
Baley had sat down during the course of his last speech and now he tried to rise again, but a combination of weariness and the depth of the chair defeated him. He held out his hand petulantly. ‘Give me a hand, will you, Daneel?’
Daneel stared at his own hand. ‘I beg your pardon, Partner Elijah?’
Baley silently swore at the other’s literal mind and said, ‘Help me out of the chair.’
Isaac Asimov, The Naked Sun, 1957
Conversational
machines in SF are surprisingly good at understanding speech spoken by
human beings. By this I don’t just mean
good at recognising the words that were spoken, nor good at working out the
meaning of utterances, though both are true, but also good at determining what
the speaker wants from the communication.
However, in this example
Robot Daneel Olivaw, a humanoid robot indistinguishable in appearance to a
human being, seems to be having a problem understanding what Elijah Baley
wants. The problem the robot has is not
recognising the words, nor in calculating the meaning of the words, but in
working out why Baley would have said those words. It is just a characteristic of human communication that we don't
always encode information or requests for information in the simplest way. Baley could have said "Robot, help me
stand up", which would have been clearly a request for help directed at
the robot. Such direct requests between
humans are considered impolite, so someone might say instead "Could you
help me stand up?" which is not a request for help at all but a request
about the availability of help. The
phrase "Give me your hand, will you?" is one more stage removed,
where even the point of the request is missing.
This additional
processing of utterances beyond their literal meaning is called
pragmatics. Listeners ask themselves
not just what is the meaning of an utterance, but why would the speaker have
spoken such an utterance? The listener
needs to consider both its meaning and its implications. The problem however, is how can a robot,
with very different concerns about the world to a human being, appreciate the
circumstances under which a human might have chosen to speak a particular
utterance. In general a single
utterance can have many implicatures – potential implications, and choosing
between them is not easy. A man saying
“this shirt is frayed” may be wanting someone to bring him a new shirt, or
suggesting that part of the household budget should be directed to the purchase
of new shirts, or indicating that he feels that he is not being cared for. What is probably not being communicated is
the simple fact that his shirt is frayed!
A robot servant that replied "OK, fact stored" would not be
doing his job. But to really understand
the implications of the statement requires that the robot knows about things
like: frayed shirts are unsightly, frayed shirts give an impression of poverty,
frayed shirts may indicate that the wearer is a bachelor, this man is concerned
that not enough money is being spent on his clothes, this man is unhappy that
no-one cares about him enough to be concerned for his appearance. And these implicatures themselves have other
implications: why is he worried about his appearance? Why hasn't he done
anything about it himself? Our robot
seems to need to know a lot about the world and human motivations and desires
just to understand a single simple utterance.
While significant
progress has been made in speech recognition (witness the availability of
dictation systems) and some progress has been made in the linguistic analysis
of sentences (used in natural-language interfaces to computer systems), much
less progress has been made in a computational theory of pragmatics. It is easy to see why. The working out of the implications of an
utterance needs knowledge about the world beyond that represented in the
sentence. This is not knowledge that is
available by an algorithm applied to a series of sentences, but it is knowledge
that must either be learned or programmed separately. Our robot needs to know enough about the context, about the real
facts of the situation within which the utterance was made. It is an issue about world knowledge, not an
issue about language. Such knowledge
just doesn't exist in a form that can be programmed into a computer, nor do our
computers yet have the ability to learn such facts from their own experience.
Attempts have been
made to build systems for interacting with people in a conversational
manner. Chatterbots are computer
programs that engage in simulated conversation with a user through typed
interactions. The aim of a chatterbot
is to fool users into thinking that they are interacting with another human
being, following the idea of Turing’s “imitation game”. The development of chatterbots (or chatbots)
comes from the growth in Internet chat rooms, where users can type messages to
each other, often complete strangers.
The idea came that this was an opportunity to build programs that could
participate in chat rooms. Each year
there is a competition to choose the best chatterbot, called the Loebner
prize. For the prize, human judges
attempt conversations with the programs, and rate them on a scale of
human-ness.
The general structure
of chatterbots seems very simple, and on the whole has not changed since the
earliest conversational programs like Eliza.
Conversations are treated as stimulus-response pairs, where a remark by
the human triggers a potentially relevant output from the computer. Many tricks are used to fool users into
thinking that the program understands what they are saying, for example if the
user asks ‘Who is the president of Brazil?’ the system may reply, “I don’t
know, do you?” By trying out the
program with many users, the system designers can see what are common forms of
interaction and build in pattern-matching machinery to convert a question into
an answer. The largest chatterbots have
tens of thousands of pattern match rules.
More intelligence can be simulated by letting the chatterbot learn from
the interactions, and to let users refer back to previous turns. These functions require a modest amount of
linguistic processing to extract and store information and to know how to deal
with anaphora (using a word like ‘it’ to refer back to something discussed
earlier).
Surprisingly, it seems
that many users are prepared to ‘suspend their disbelief’ and treat these
programs as actual conversational partners, even confiding personal information
to the programs. However any extended
conversation on a topic rapidly shows that the program has no real knowledge of
any subject and no understanding of what a conversation is about. Conversations rapidly deteriorate into
trivial, contentless exchanges of fixed phrases.
Chatterbots are
designed to hold conversations but in fact only simulate the use of words in
conversations. There is no exchange of
information, no co-operative problem solving, persuasion, argument, agreement,
consolation, mutual regard, or any other non-trivial aspect of human-human
conversation.
Dr. Calvin said softly, "How are you, Brain?"
The Brain's voice was high-pitched and enthusiastic, "Swell, Miss Susan. You're going to ask me something I can tell. You always have a book in your hand when you're going to ask me something."
Dr. Calvin smiled mildly, "Well, you're right, but not just yet. This is going to be a question. It will be so complicated we're going to give it to you in writing. But not just yet. I think I'll talk to you first."
"All right. I don't mind talking."
Isaac Asimov, Escape!, Astounding Science Fiction, 1945.
So far we have
considered four aspects of language technology: speech input, speech output,
linguistic analysis and pragmatic analysis.
But how do these fit together to create a conversational machine? A fifth aspect must be how the system
demonstrates its understanding through behaviours triggered by the act of
communication. A machine that sat and
hummed while you spoke to it would not seem to be understanding. Of course one way in which understanding can
be shown is for the machine to take the appropriate action: to obey a command to
take some physical action, to speak a message that is a coherent response to a
request for information, to explicitly acknowledge that a request is
understood. But there are other
properties of human-human dialogue that a machine might do well to imitate.
Firstly, human
participants in a dialogue use various mechanisms to demonstrate, while the
other person is speaking, that they are following the argument, that they are
interested, that they want to hear more.
This kind of feedback can involve things like eye contact, head
movement, body posture, or vocalisations like "Mm", "I
see", "Uh-huh". These
activities help control the communication process; they are a way of
communicating meta-level information about the success of the dialogue.
A defining characteristic
of dialogue is turn taking; but even here it is not always obvious when one
person should stop speaking, or when the other should start. Normal dialogue is not like half-duplex
radio communication where the participants say "Over" when they want
to surrender control to the other party.
Surprisingly it is not even necessary for one person to pause before the
other person speaks – dialogue often has many instances of simultaneous
overlapping speech. The cues that a
speaker gives to say they are willing to give the other person a chance to
speak are not just when an utterance reaches its logical conclusion. Sometimes turns can be suggested by phonetic
changes in pitch or speaking rate.
Part of the problem of
analysing turn taking is that it is built on a relatively ill-researched area
of co-operative problem-solving. A
dialogue is an interactive language game that has its own conventions and
rules. The rules aid the participants
in making sense of the utterances, just as knowledge of the world helps a
listener understand the implications of a sentence. There have been suggestions for conventions like: quality,
quantity, and relevance. The listener
should assume that speakers produce utterances based on what they believe to be
true, that they are providing only the significant information, that what they
say is relevant to the argument. Such
rules can be broken to achieve certain effects, for example in “damning with
faint praise” a true but understated fact has wider implications, as in “That
student was always very punctual” – implying that he had few other worthy
attributes.
There has been little
progress on the computational modelling of these subtle aspects of
dialogue. Most existing man-machine
dialogue systems rarely rise above a question-answer format. Take speech enquiry systems as an
example. Speech enquiry systems
comprise a speech recognition system to convert speech signals into word
strings, a speech synthesis system to convert text to speech, a database system
to hold information required by the user, and a component called a ‘dialogue
manager’ that mediates between the components to create a system that can
answer queries posed by voice. Typical
applications would be for flight enquiries, account enquiries or cinema ticket
booking. Most of these systems have
been designed to answer queries posed by the general public over the telephone
and this makes severe demands on the technology. For example the speech recognition component must deal with a
significantly poorer quality input signal from previously unknown
speakers. There are not the
capabilities to enrol the speaker nor learn from mis-recognition. One way to compensate is to restrict the
system to a very narrow domain of discourse.
Thus any given enquiry system can only deal with spoken language about a
particular task. These restrictions
have effect throughout the system: firstly the recognition system can be
limited to certain words and a simple grammar; secondly the dialogue manager
may have a fixed sequence of interactions; thirdly the synthesis system may
only need a finite vocabulary of phrases.
While users stay within the restrictions and expectations of the system,
performance is adequate for simple tasks like flight times or ticket
reservation. The systems are however
rather fragile, and when users stray outside the capabilities of the system (by
using words outside the vocabulary, by being disfluent or unclear, by speaking
over background noise, by speaking over the system’s prompts, by failing to
answer questions with just the information expected, etc) communication can
rapidly break down. Such systems work
best when the computer takes charge of the dialogue, asks simple questions and
gets direct answers. This requires that
users themselves have to be aware of the limitations of the system.
Speech enquiry systems
are perhaps the closest complete system to a conversational machine, but the
goal-directed nature of the dialogue, an inability to obey conventions in turn
taking, frequent mis-recognitions requiring multiple conformations – all lead
to an impoverished experience.
Manufacturers usually allow users to operate such systems with tones
from the phone keypad instead of using speech if they wish.
[Robot AI Sven wants a better battery …]
"Sounds good to me. We'll get one."
"I have already ordered it in your name and it was delivered this morning."
"What? Isn't
that a little high-handed?"
"Dictionary definition of high-handed, an adjective meaning
overbearing or arbitrary. That is not
an arbitrary decision but a logical one that you have agreed with. Overbearing is defined as a domineering
action or behaviour. I did not attempt
to dominate, therefore do not understand the application of this word. Could you explain …"
"No! I take it back. A mistake, right? We need the battery, I would have ordered it in any case, you merely helped me out. Thanks a lot."
Brian regretted the last, but hoped that Sven's phonetic discriminatory abilities weren't that finely tuned yet to enable it to determine the presence of sarcasm by the inflection of words. But he was sure learning fast.
Harry Harrison & Marvin Minsky, The Turing Option, Warner
Books, 1992
In SF it is rare to
see a child robot learning to become an adult robot. Since robots are manufactured rather than grown, they are born
with the physical abilities of an adult, and authors seem to infer that this
also means they are born with the mental abilities of an adult. Most SF computers and robots that use
language also seem to have their language skills 'built-in', they do not seem
to need to learn how to use language the way humans do.
There are perhaps
three ways a robot could get a language faculty: (i) it could learn language
itself, individually, through experience; (ii) it could be given a copy of the
artificial brain of a different robot that had already learned language; or
(iii) it could be programmed to use language by its human designer. None of these seem ideal. The first alternative means that the robot
manufacturer would have to set up robot schools to teach them basic skills; the
second means that all robots would have the same language performance (and
memories); the third means that we will have to understand language much better
than we do now. If we have to build a
watertight computational model of language before we build a robot, then we may
be waiting a long time.
Actually the trend in
language technology has been away from explicit programming of language skills
towards machine learning algorithms that look for and exploit regularities in
language that have been uncovered by statistical analysis of a large corpus of
language examples. There is already a
shift, then, towards providing our computer systems with the ability to learn
how to use language, rather than with the facility directly. However progress here is limited by the type
of language data available. These tend
to be large collections of text collected from books and newspapers. They are not collections of linguistic
interactions in which the meaning and implicatures of each utterance have been
exhaustively established in some machine-compatible form. Thus what these systems learn is about the
surface form of language, they do not learn anything about what it refers to.
You get a sense of
what is lacking in such superficial analysis of language when you use an
information retrieval system to find documents based on an enquiry. Using the Google search engine to find pages
on the worldwide web for example. The
documents that are calculated to "match" the phrase you type are only
related in the sense that they contain the words you used. This literal minded search leads to a number
of weaknesses: (i) words have multiple senses, so that some documents containing
the words are about something completely different, for example a search for
"speech" finds instances of politicians' speeches; (ii) documents are
treated as bags of words, so that there is no necessary match of meaning, for
example a search for "flights to Barcelona" will find documents about
flights from Barcelona; and (iii) the same sense can be described by
multiple words, so that some documents that are extremely relevant are not
returned because they don't happen to use the words you used, for example a
search for "oil-paintings" may not match documents describing
"old-masters".
Of course a
superficial statistical analysis of language has advantages: it is very fast,
it has a low computational overhead. And importantly, since there is not yet
available a deeper analysis of good quality, it is the best method we currently
have for some tasks.
Scene 83: PLANET SURFACE
Jason looks up to the rock ledge. The unseen DEMONS continue to
chant "GORIGNAK... GORIGNAK..."
JASON: Wait, the pig lizard is gone. Why are they still chanting for the
pig lizard?
GWEN: Turn on the translation circuit.
Teb flicks a switch and we hear the Demons in English.
DEMONS: ROCK... ROCK... ROCK...
David Howard, Galaxy Quest, 1999
Computer systems in SF
are particularly useful for translating alien languages. How they do this in the absence of a corpus
of analysed and labelled samples is never made clear. Not only does an alien have unknown communication hardware,
unknown phonetic coding, unknown lexicon and unknown syntax, it also has an
unknown culture within which the elements of language are grounded. A few minutes listening to some sounds that
they make will not get you very far in terms of understanding.
But even in
translation between human languages, where there are many resources such as
bilingual dictionaries, grammar studies and even parallel corpora, automatic
translation systems still leave much to be desired. Some problems arise from the ambiguity of language: where one
utterance can have many literal meanings.
But other problems are related to the fact that a good translation is
not just a representation in another language of the literal meaning of a
sentence, but a representation that carries the same implications. While it may be the case that some literal
translations have the same implications, there are many instances where a
literal translation leads to startling results. This is a rich vein of humour, such as the translations into
English of notices in foreign hotels: such as a Finnish hotel's instructions in case of fire:
"If you are unable to leave your room, expose yourself in the
window".
This survey has shown
that there remains an enormous gap between the capabilities of science fact and
the conversational machines of science fiction. In Speech Recognition there are still problems in recognising
speech in everyday situations, where an unknown speaker is speaking
spontaneously and in a noisy environment.
In Speech Synthesis there are problems in generating a prosody that
matches the information structure and communication goals for an
utterance. There is also the problem of
how to give a machine a vocal tract of its own, rather than to re-use recorded
speech. In Grammar and Meaning there
are problems in dealing with the ambiguity of language and the difficulty of
finding the most appropriate meaning for an utterance without knowledge of the
context within which it was uttered. In
Pragmatics there are problems in determining the implications of utterances
beyond their literal meaning, involving the machine being able to understand
why someone would have said such a thing.
In Conversation there are problems in following human conventions in the
structure and execution of dialogue. In
Language Acquisition there are problems in grounding language in real experience. And lastly, in Translation there is the
problem that to translate is to know all of these things in two language
cultures.
In the absence of
actual conversational machines, science fiction writers have modelled the
linguistic abilities of their creations on the linguistic abilities of
humans. However this generates
unrealistic expectations about the future of language technology. Since machines will not be human, and since
human language is so fundamental to what it means to be human, it is unreasonable
to expect a machine’s use of human language to be human-like. As I have indicated, some aspects of a
machine's use of human language in SF are just silly. There is no necessity for a machine to have a human vocal tract
or human emotions. But the gap between
imagination and reality does expose the significant problem in getting
artificial systems to understand human language in anything like the way that
humans do. If we were to sum-up the
weakness of current technology in one phrase, then it would be that our systems
process only the surface form of language without understanding what it means.
What would constitute
understanding by a machine? In this
essay I have implicitly assumed that understanding can be broken down into four
stages: a recognition stage where sounds are decoded into word strings,
a syntactic-semantic stage in which a number of possible literal
meanings are established, a pragmatic stage in which the intended
meaning and its implications are resolved, and finally a communication
stage which demonstrates that the utterance has been processed and
understood. This is the conventional
way of thinking about language processing, and it is predicated on the
assumption that understanding is fundamentally a linguistic
problem. On this basis, a
conversational machine would emerge once we had programmed a computer to
achieve human performance on all of these stages. If a computer were able to process an utterance through these
stages as a human would, we would admit that the computer had understood the
utterance.
I think the actual
challenge we have is different. In some
senses our machines can already process utterances through these four stages of
understanding. It is just that they are
very limited on the type of utterance they can process, and their overall
accuracy is rather poor. Our current
systems seem to make elementary mistakes of word recognition, meaning and
inference. They have human-like adult
voices but sound as if they have no understanding of what they are saying. The problem I think is that we expect
machines to have a human level of performance.
We program into them the complexity we recognise in adult human language
without concern for how this prevents understanding being achieved. Thus we ask our dictation systems to
recognise text they couldn’t possibly understand, or our text-to-speech systems
to read out text that a human has written.
The idea, of course, is to create a language technology that is useful
to us. But in requiring adult human
competence we hit up against the significant problem that human language is
alien to a machine.
In this essay I have
tried to show that human language is perhaps more peculiarly human than
most technologists and SF writers have accepted. The quality and structure of speech, the encoding of meaning, the
decoding of implicatures are essentially founded in the physical and mental
limitations of humans and their specific concerns on this planet. Machine intelligences, when they arise, will
not have such concerns, and their view of the world will not be the same as
ours. As a consequence they way in
which they use language will be alien to us as our use of language will be
alien to them. The best we can hope for
is that we can learn to adapt to their way of thinking and vice versa. This will not allow us to communicate in our
natural language, but perhaps communicate enough to get some useful jobs done.
Current technological
development to program machines with human language, then, will simply lead to
superficial approximations and simulations of language. We will be trying to build an alien-human
translation system when the two cultures do not share the same world
model. This is not to say that
translation of sufficient quality could be achieved to be useful for some tasks,
perhaps in limited domains. But to
create a machine with understanding of both breadth and depth requires that the
machines develop their own translation systems. Through experience with the world and interactions with humans
they would develop a mapping that was consistent with their own experience,
even if that was alien to our experience.
We need to let
computers learn themselves how best to use human language to achieve their
objectives.
If we give them a need
to communicate, a desire to communicate, then this will force them to develop a
version of human language of their own, a voice of their own. Of course it will be a modified form of
human language, but with its own peculiarities of lexicon, syntax, semantics
and pragmatics. When we hear it we’ll
recognise that we are talking to a machine.
When the computer speaks it will be in utterances that it really
understands, because they will be utterances it will have made up itself to
meet its own demands for communication.
When the computer listens, it will only accept utterances from us that
it understands; utterances that meet its preconceptions about the topic of
conversation. When we use human
language that it can't understand we won’t consider that to be a weakness of
the computer – it will be a normal error of translation between cultures.
How can we arrange for
a computer to learn a version of human language that meets its own
communication requirements? Only by
letting the computer learn language the same way that human children learn language,
through experience. We should be
programming computers to learn how to communicate, not attempting to build into
them adult human language.
Science fiction
writers got it wrong when they assumed that future computers and robots will
use human language the way that humans do.
But they got it right when they also gave those computers and robots the
ability and desire to communicate. The
“personality” of these machines expressed in their use of language will not
come from limitations of how their language sub-system was programmed, but from
how they have learned to use language in pursuit of their own goals.
Dictation programs are
available from a number of vendors. The
program ViaVoice by IBM is described at: http://www.software.ibm.com/speech/. The program NaturallySpeaking by Dragon
Systems is described at: http://www.scansoft.com/naturallyspeaking/.
The earliest chatterbot
program was called Eliza, who took the role of a psychotherapist, see:
J.Weizenbaum, Eliza – a computer program for the study of natural language
communication between man and machine, Communication of the ACM, 9
(1996), 36-45. The Loebner prize competition
for the best chatterbot each year is described at the web site: http://www.loebner.net/Prizef/loebner-prize.html. The winner in 2000 and 2001 was a chatterbot
called Alice, which is described at http://www.alicebot.org/.
Paul Grice’s
conversational maxims are described in: H.P.Grice, Logic and conversation, in Speech
Acts ed P.Cole and J.Morgan, Academic Press 1975.
Microsoft’s database management
software SQL Server 2000 has the facility for posing database queries in
English. This is described at: http://www.microsoft.com/sql/evaluation/features/english.asp.
A number of
text-to-speech systems can be tried out over the web. Some can be found though the meta-site www.speechandhearing.net.
Isaac Asimov's
collection of robot short stories is published as "The Complete
Robot", Voyager Books, 1995. Isaac
Asimov died in 1992.