Genre: Information/Explanation

The possibility of Natural Language Processing by computer by Dave Inman Computers and language Many claims are made of the ability of computers to process and understand language, usually referred to as natural language processing (NLP). We see new products such as voice recognition software becoming available for our PCs. The press fuels the hype, predicting within a few years computers which will understand us on our terms; through natural language. Such claims are however false for a few fundamental reasons. The problems that exist in the NLP endeavour are so serious that it may be several decades before any real progress is made for unrestrained language processing. Science fiction has also been too optimistic in estimating progress towards NLP. An example from over 30 years ago, "2001: A Space Odyssey", made predictions for computers used at the turn of the century. One of these, HAL, was able to have meaningful dialogues with the astronauts. Speech recognition and understanding together with psychological advice were packaged into a friendly mentor. Interestingly whilst chatting with HAL, the astronauts would use a clip board and pen to monitor the state of the space ship. Today microprocessors, invented 5 years after the book was written, monitor and control car engines thousands of times a second and yet our PC cannot understand a sentence such as "Can you delete this file?" We may get the infuriating reply "Yes" if we intended a polite command, or worse the reply "Done" to a genuine question about a possibly locked file. Why is NLP so hard? To answer this question we could look to Winograd and Flores (Winograd and Flores 1995). Winograd identifies ambiguity as being of key importance to the problem of NLP. When we as humans process language we are continually making guesses about meaning using our rich knowledge of the world and of the current culture to try and work out what is being communicated. An example from Winograd illustrates this nicely. If asked "Is there water in the fridge?", most humans would consider if there were something like a bottle of mineral water in the fridge. The existence of water molecules in the lettuce would be considered a "funny circumstance" outside the normal cultural use of the question. Of course we are not restricted to asking such questions, but they must be made explicit. A child would need to ask, "Is there water in the lettuce?" explicitly. Winograd states that we use language with a rich knowledge of "normal circumstances". When using language in these situations, we do not need to state them explicitly. They are assumed to be known to all communicators. This unspoken context may comprise 90% of a communication, and allows the language which builds on this foundation to be very concise. Imagine if we visited a friend on a hot day and asked "Do you have any water in the fridge, excluding the molecules in any vegetables or fruit, or the molecules in any containers or structural components of the fridge, as well as in the coolant of the fridge....?" And yet without such unspoken explicit context the computer is lost. Computers are not alone in finding context or cultural understanding a problem. Adult foreign language learners cannot become proficient without such understanding. Whilst many contexts, or normal circumstances, are transferable between cultures, even subtle difference can create problems of understanding. Of course it is one of the great pleasures to discover different ways of looking at the world, and we as humans can discover and hold multiple views of "normal circumstances". This might be one of the reasons why foreign language learners and teachers are such good company. But computers cannot hold even one reasonably rich view of context, culture or normal circumstances. For a computer everything is an endless muddle of possibility with almost no way to sort out the "normal" from the "funny" circumstance. How can a computer deal with context. There are three broad approaches; pretend there is no problem, use human cognitive abilities to help a computer, or try and represent context within computer systems. All are inadequate if we wish to hold a reasonably free dialogue with a computer. The blinkered approach. The idea here is to pretend that language has no ambiguity. Only a computer programmer could have had such an idea. Thus if we have ambiguity, a computer assumes only the context allowed by the programmer. A database on chemicals would allow questions about whether there is water in chemical X with no knowledge that water can also be used by humans for drinking. Provided humans know this context, and work with it, this can be an acceptable solution. But even programmers become annoyed when computers cannot deal with even the slightest typo in inputting a program to a computer. Such a brittle approach is ultimately so frustrating to use that the goal of natural interaction with a computer seems distant. The co-operative approach. Here we do not need to rule out ambiguity, but rather recognise that it exists and ask a human to disambiguate. For example in a machine translation system, pre or post processors of the text ask the human to explicitly describe which of several interpretations is correct. The problem here is that such a dialogue becomes extremely wearisome, given the inability of the computer to sort out ambiguity. The more contexts we allow, the more annoying this dialogue becomes. To use such a system makes it clear to us the enormous range of possible interpretations of language, and makes us wonder at our ability to deal with this so successfully. The common sense approach. Some attempts have been made to give a computer a knowledge of the world. An example is Doug Lenat's CYC project (Davidson 1994) at the University of Austin , Texas, which took more than 12 years of effort. This $35 million project begun in 1984, uses a modified form of logic and frames to represent encyclopaedic knowledge. So called "knowledge engineers" were employed to input tens of millions of common-sense facts. Lenat's goal was to use this knowledge to reason using common sense, and to use it for natural language processing. Many problems exist however. The huge number of facts creates a problem of reasoning with them quickly, and there is a lot of evidence that humans do not use logic in the same way as the CYC project does. Ultimately the arguments revolve around whether a symbol manipulator such as a computer can ever know about the world as we do. Semantics can never be attached to symbol manipulators is the strongest argument against systems such as CYC. No effective, free input NLP systems based on the CYC system have been demonstrated to date. NLP for CALL (Computer Assisted Language Learning) So what has NLP to offer the foreign language learner and teacher in the near future? Certainly not the ability for human-machine interaction in a natural free manner. Our dialogue with a machine would either be continually interrupted with requests for clarification and disambiguation or frustrated by the machines limited range of interpretation and frequent mistakes in interpretation. But this is not all gloomy. Whilst a PC user may want a natural, fast interface such as speech recognition and understanding, for a foreign language learner a dialogue with a machine might be desirable. They would be exposed to accurate language fragments, and would see a range of possible interpretations, not all of which might have been known to them. Furthermore, the feelings of inferiority that language learners often experience with native speakers might be mitigated by such dialogues with a machine. The huge cognitive advantage we have over machines might help to build confidence, especially as the communication will be seen to depend on the foreign language learner. The slow speed of the dialogue might not be such a problem for a language learner who often needs time to reflect and absorb new material. Conclusions The possibility of free natural communication with a machine in the near future seems unlikely despite the hype. There are fundamental problems that require a huge step up in a computer's ability to represent the world, and the different contexts and cultures within the world. Whilst anthropologists and psychologists have studied culture and context extensively, current computer representations are of very limited value, and seem unlikely to assist computers in the NLP endeavour. The use of computers in CALL however seems to offer real benefits to students. Computers can offer real advantages in composing text, and on-line help such as dictionary support becomes very useful. Whilst simple technologies like these are practical and useful to students, more ambitious assistance, such as providing some contextual support through video, has some limitations due to the bandwidth required. These difficulties will disappear over time as bandwidth increases, perhaps through the use of satellite communications. In the longer term, the way forward may be through co-operative NLP systems, where the learner and computer help each other in the rich and exciting world of natural communication. Computers highlight our own incredible ability to use the minimum of language, and the maximum of cultural and contextual background in our endeavours to understand and to be understood. References Davidson, Clive, "Common Sense and the Computer", New Scientist, 2 April 1994