What Computational Linguists Can Learn from Psychologists (and Vice Versa)
Emiel Krahmer · Computational Linguistics · 2010
Sometimes I am amazed by how much the field of computational linguistics haschanged in the past 15 to 20 years. In the mid-nineties, I was working in a researchinstitute where language and speech technologists worked in relatively close quarters.Speech technology seemed on the verge of a major breakthrough; this was around thetime that Bill Gates was quoted in Business Week as saying that speech was not justthe future of Windows, but the future of computing itself. At the same time, languagetechnology was, well, nowhere. Bill Gates certainly wasn’t championing language tech-nology in those days. And while the possible applications of speech technology seemedendless (who would use a keyboard in 2010, when speech-driven user interfaces wouldhave replaced traditional computers?) the language people were thinking hard aboutpossible applications for their admittedly somewhat immature technologies.Predicting the future is a tricky thing. No major breakthrough came for speechtechnology — I am still typing this. However, language technology did change almostbeyond recognition. Perhaps one of the main reasons for this has been the explosivegrowth of the internet, which helped language technology in two different ways. Onthe one hand it instigated the development and refinement of techniques needed forsearching in document collections of unprecedented size, on the other it resulted in alarge increase of freely available text data. Recently, language technology has been par-ticularly successful for tasks where huge amounts of textual data is available to whichstatistical machine learning techniques can be applied (Halevy, Norvig, and Pereira2009). As a result of these developments, mainstream computational linguistics is nowa successful, application-oriented discipline which is particularly good at extractinginformation from sequences of words.But there is more to language than that. For speakers, words are the result of acomplex speech production process; for listeners they are what starts off the similarlycomplex comprehension process. However, in many current applications no attentionis given to the processes by which words are produced nor to the processes by whichthey can be understood. Language is treated as a product not as a process, in theterminology of Clark (1996). In addition, we use language not only as a vehicle forfactual information exchange; speakers may have all sorts of other intentions with theirwords; they may want to convince others to do or buy something, they may want toinduce a particular emotion in the addressee etc. These days, most of computationallinguistics (with a few notable exceptions, more about which below) has little to say