Phil Resnik's ACL keynote: A new balancing act

On July 5, Phil Resnik delivered a keynote address at the 64th Annual Meeting of the Association for Computational Linguistics, with the title " A New Balancing Act: Reflections on the Relationship between Computational Linguistics and AI ":

In this talk I argued that the field of computational linguistics – a term that includes NLP as its engineering-research subdiscipline – is experiencing a “success catastrophe”. The commercial success of LLM-based AI has thrown three key aspects of our research community out of balance. Here are three balancing acts we face:

First: Like any research community, we can recognize and take advantage of the knowledge obtained in earlier generations of work; we can also lean into new approaches.

Second: We can focus on language as language, which is to say, the properties of language that make it distinctive and human; we can also treat language as an input/output modality for AI systems.

Third: We can emphasize our role as a research community, where our primary purpose is to contribute to the stock of human knowledge; we can also emphasize our role in making sure that our young members have a path forward to get jobs – particularly jobs in industry since the path into academia is never a sure bet and for many of them industry is the goal.

In each of these pairings, the central importance of the former has given way to the overwhelming dominance of the latter.

Moreover, because of the concentration of power and the convergence on methods that require enormous resources, our natural corrective processes – the historical pendulum-swings back and forth – are fundamentally broken. We have lost scientific pluralism.

These losses of balance are bad for the research enterprise, where the explosion of “let me demonstrate my skills to use LLMs and move a score” submissions has brought quality control via peer-review to its knees.

These losses of balance are bad for society, where deployment of systems without a solid understanding of their underlying theories has already caused significant harms to people.

And, I argue, these losses of balance represent an existential threat to our community. AI and LLMs are now inextricably a part of what we do. But if that’s all we do, if we abandon our core value of contributing to human scientific and engineering knowledge about language as language, then we lose who we are. We become just another entry on the list of machine learning conferences.

Phil calls this a "new" balancing act, in reference to a workshop and book from 30 years ago, published as " The Balancing Act: Combining Symbolic and Statistical Approaches to Language ", Judith Klavans & Philip Resnick, eds.

Metaphors involving balancing and pendulums can be found in both the old and the new "balancing act" texts ( read and hear the whole new thing — the old one is dying on library shelves and in used book stores…).

The intended interpretations of these balancing/pendulum metaphors are certainly valid and useful, but in " The Future of Computational Linguistics: On Beyond Alchemy " (2021), Ken Church and I sketched four socio-scientific eras, and suggested a different metaphor, namely seasonal migration of herbivorous herds:

This description suggests a winner-take-all picture of the field. In fact, 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. Logic played a larger role when rationalism was in fashion, and probability played a larger role when empiricism was in fashion, and both logic and probability faded into the background as deep nets gave procedural life to an associationist (rather than statistical) flavor of empiricism. But at every stage, there have been research communities of various sizes inhabiting or exploring different regions of this dynamic landscape, motivated by their various ideological visions, their preferred methodological tools, and their substantive goals. The decades have seen various different communities prosper, decline almost to extinction, and then grow again, waxing and waning in different rhythms. The seeds of the next dominant fashion can always be seen in research communities that seem marginal at a given stage.