Abstract
Over the decades, fashions in Computational Linguistics have changed again and again, with major shifts in motivations, methods and applications. When digital computers first appeared, linguistic analysis adopted the new methods of information theory, which accorded well with the ideas that dominated psychology and philosophy. Then came formal language theory and the idea of AI as applied logic, in sync with the development of cognitive science. That was followed by a revival of 1950s-style empiricism—AI as applied statistics—which in turn was followed by the age of deep nets. There are signs that the climate is changing again, and we offer some thoughts about paths forward, especially for younger researchers who will soon be the leaders.
Highlights
We are going to speculate about the future of Computational Linguistics (CL)—how things may change, how we think things should change, and our view of the forces that will determine what happens
Given that different people have different views of what the field is, and even what it should be called, we will define the field of Computational Linguistics by what is discussed in top venues, using Google Scholar’s ranking of venues
The current name dates back to 1968.2 Before that, the name of the society included the phrase, “Machine Translation,” a topic that was more popular before the ALPAC report (Pierce and Carroll, 1966) than after the ALPAC report, especially among funding agencies in America, for reasons described by Hutchins (2003) among others
Summary
We are going to speculate about the future of Computational Linguistics (CL)—how things may change, how we think things should change, and our view of the forces that will determine what happens. The field has changed directions a number of times for a number of reasons, as will be discussed below. Given this history of change, it is likely that there will be more changes in the future. The field has always benefited from a give-and-take of interdisciplinary ideas, making room for various combinations of methodologies and philosophies, in different proportions at different times. Instead of a return to Rationalism, though, the rebellion took an unexpected turn with the revival of Connectionism These days, most papers in top venues in computational linguistics (as defined in footnote 1) make use of machine learning, many favoring end-to-end systems over representation (with or without statistics). We will refer to this winning combination as “better together.” It is tempting to emphasize differences, but more profitable to emphasize synergies
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