In this essay, I would like to sum up my opinions on what is the role of computational linguistics, why should people concern with it and I believe are its current problems, and most importantly why it is a very exciting field of study.
Computational linguistics is a something between a research and an engineering field whose goal is to use mathematical models to describe natural languages (like English or Czech, not e.g., programming languages) and come up with technological applications of these models. Due to these goals it is necessary a multidisciplinary field which combines something from classical linguistics, artificial intelligence, mathematics and software engineering.
The attempts for automatic processing of natural languages are with us from the early days of artificial intelligence. After all, the famous Turing test, which is sometimes considered to be a criterion of artificial intelligence being really intelligent, assumes that an intelligent machine must be able to communicate in natural language. The artificial intelligence took a different direction in the last 50 years – most importantly it focused on attempts to model some of the most basic human cognitive abilities and development of intelligent agents trying to meet some objectives in an environment. Computational linguistics with its effort to model language without general AI sitting in the background got established as a standalone research discipline.
In the last thirty years, computational linguistics undergone a fascinating development when it transformed from an almost obscure research field into to solving practical engineering problem. At the same time, it somehow diverged from its parent field – formal linguistics whose goal is to use formal (algebraic) apparatus to describe language phenomena. It diverged so much that it seems that the fields can hardly enrich each other anymore, which is according to me a big pity.
Linguistics as Almost a Natural Science
Contemporary linguistics stressing descriptive (describes the phenomena as they are and does not prescribe how they should look like) and synchronous view (describes the current state of language and disregards the history the language user don’t need to know to use speak) of language is very similar to natural sciences. This is also a suitable view for technological applications. When I am building a machine translation system, I don’t care so much how people should spell something and what are the historical reasons for that, what I need to know is how do the sentences they the users want to translate look like.
Linguists usually use so called language corpora for their theoretical work. These are databases of texts collected form various sources. Mostly, they are newspaper and magazine texts (journalists are generating loads of text every day) and both fiction and non-fiction books. Linguists try to collect data also from other types of language use, including for instance spontaneous speech or communication on social networks. In addition to that, various phenomena are either manually or automatically annotated in the texts, such that the linguists can analyze them both quantitatively and qualitatively. For acceptance of a scientific hypothesis, it is no longer important to persuade other linguists with their language intuition, but statistical significance of explaining the phenomenon in the corpus.
Natural sciences often methodologically rely on some version of Karl Popper’s critical rationalism. What is important for scientific hypotheses is their openness to future falsification. We consider a scientific theory to be valid if it has not been empirically (usually experimentally) falsified even though scientists have tried as hard as they could. The theory is considered valid until some contradictory observation are made, but in some sense it remains valid even after they are falsified. Discovery of the theory of relativity of course didn’t make fall all bridges whose stability had been computed using Newtonian mechanics. It didn’t even change the way, the stability of bridges is nowadays computed (except that slide rules was replaced by computers). To summarize, if a scientific theory is in contradiction with the empirical observations, scientists need to search a for a new theory that will explain the observations in a more precise, indisputable and more elegant way. It does not, however, affect the explanative power of the previous theories even though they may have been proven wrong.
Linguistics works very similarly to natural sciences in this manner. It tries to build such theories that could be statistically tested on the collected observations – language corpora. In addition to that, they collect more and more data which can potentially show some problems in the current theories.
Theories considered “real”
Scientific theories often come up with theoretical entities which can seem very bizarre at the time of their discovery, but after some time start to believe they are real – not only the scientific community, but also the public.
For acceptance of newly postulated theoretical entities in the scientific community, it is important that it elegantly solves problems they had in the previous theoretical framework of the field. Assumption of a new entity usually allows explaining previously inexplicable observations, or explaining something more exactly or more elegantly.
For the public, there needs to be something more for the theory to get accepted. For instance, the superstring theory explains very well many experiments in the contemporary physics. In spite of that, the idea of living in the eleven-dimensional space with seven dimensions tangled up in themselves is not how public thinks about reality. On the other hand, nobody is surprised by the fact that the matter around is “in fact” empty space with elementary particles occasionally flying around. No one cares that this “reality” contradicts the natural experience with the world in the same way as seven spacial dimension mysteriously tangled up in themselves.
What I believe is crucial for public acceptance of scientific theoretical entities is existence of technological application which are based on the theories. Unlike the theoretical entities, the technological artifacts are part of our everyday reality. Hardware of the computer I am right now using to write this text is based on technologies which are unthinkable without assuming existence of elementary particles. Knowledge of the theory of relativity allowed us to develop satellite communication and navigation. Without it I would hardly believe that if traveled with a speed close to the speed of light, the time would run in different pace for me than for those who stayed home flew nowhere.
Technology is a visible and concrete part of our everyday experience – and because of that we can’t easily ignore the theoretical concepts behind them. Maybe because of that some people can easily deny the existence of global warming or Darwinist evolution which can in principle never be an underlying technology of a technological innovation.
Linguistics and Technology
Some twenty of twenty-five years ago, it may have seemed that the automatic natural language processing can provide the same assurance of the truth to the theories as engineering innovations provide to the theories in physics. Methods of natural language processing used linguistic theories during the development and the source code was usually packed with a lot of explicit linguistic knowledge. At the beginning of the 21st century, it started to appear that machine-learning based systems learning from the annotated data worked better than those where the programmers tried to put the linguistic knowledge explicitly in the source code. With the increasing availability of the data and computational power, the required amount of linguistic knowledge was gradually decreasing. At this moment, complicated systems as automatic speech recognition, machine translation or text summarization are trained from the data end-to-end with no linguistics inside. The technologies found their own way, working totally independently of the language understanding provided by linguistics.
Nowadays, we are in a unique situation where the language technologies live outside of the conceptual framework provided by classical linguistics which has some paradox consequences. Results of the experiments in physics can be predicted using the theory. However, when we do deep learning experiments in computational linguistics, there is no theory that could in advance say what the results will be. There is only the researcher’s or developer’s (usually strongly mathematically grounded) intuition which is either confirmed or not. Often, it may seem that the researcher has prepared arguments for both success and failure of the experiments and only waits for the results of the experiments.
The almost miraculous success of deep learning methods in natural language processing is so important to me, that if I were asked how languages are like, I would certainly answer they are exactly such that they can be efficiently processed by artificial neural networks. I don’t know what exactly it means and unfortunately, contemporary linguistics has no answer for that as well.
I don’t want to claim linguistic theories are useless. They provided a conceptual framework without which any meaningful discussion about language would even be possible. Linguistic theories prove their usefulness when we want to talk about some complex language phenomena and provide invaluable service while learning both mother tongue and foreign languages. Their usefulness, however seems to vanish when we try to use them for automatic processing. Somehow similarly, the Newtonian physics is a great tool for engineering. Given strange circumstances when objects move at very high speeds, the use of the theory starts to be very limited. It would be great if linguistics could find its unifying theory as physics found for mechanic that could be used both as a basis for technological applications and have the same descriptive power as the current linguistic theories.
What to Do with Computation Linguistics
While doing computational linguistic, we also need to take care of what is becoming a pure engineering which is usually done more efficiently by private companies and aim the public support to projects trying to answer interesting questions which are not of primary interest of the enterprises. Computational linguistics is experiencing something similar to astronautics twenty years ago. The exclusive government-funded engineering field became a technological business as any other. Private communication satellites are flying around the globe, although the outset of space technology wouldn’t be possible without generous public support.
I believe that public support is crucial for development of complex technologies. The Internet has its origin in a U.S. army project. The success of deep learning would be possible without long-lasting generous support of Canadian government. On the other hand, it is important to capture the right moment when the public support is no longer indispensable for the technology as for instance in case of the satellite communication.
It is no longer true that machine translation is a basic research, whose public support can bring groundbreaking results. It is neither automatic syntactic analysis, nor speech recognition – these are all well-managed engineering tasks where big technological companies have better results than academic institutions. Improving the performance of the application doesn’t have to be to main part of computational linguists’ work. Working on application have of course the advantage that you can easily show some quantitative progress. Reducing error rate in an application is a clear result you can report to your grant agency – which is doable until the grant agency eventually finds out that what you do is no longer basic research but pure development. (And being a developer is a great intellectual adventure as well!) This situation is also convenient for the companies – publicly funded research generates innovation which they can easily monetize.
It does not mean that computational linguistic as a research field should leave academia and move to entirely to industry. In fact, academic computational linguists should be happy that they no longer need to focus on technological innovations (and if they want, they have where to go). The researchers have now a unique chance to use and develop the technologies in such a way that would gather new observation about language that will eventually lead to new linguistic theories that would both play the role of the current linguistic theories and explain the experience which we get from the automatic natural language processing.
Moreover, computational linguistics experts are still needed in academia to train new generations of experts and via they public activity ensure that there will be always public control over the new technologies and their potential misuse.