Jason Eisner, a professor of computer science and a member of the Center for Language and Speech Processing, has been elected as a Fellow of the Association for Computational Linguistics. He was cited for his significant contributions to probabilistic models and algorithms for finding linguistic structure, especially lexicalized syntax and morphology.
Since 1962, the ACL has been the main international scientific and professional society for scholars working on computational problems involving human language. Its Fellows program, established in 2011, recognizes members whose contributions have been extraordinary in terms of scientific and technical excellence, service to the association and the community, and/or educational or outreach activities with broader impact.
Eisner’s research goal is to develop probabilistic modeling, inference, and learning techniques capable of modeling all kinds of linguistic structure and connecting with existing models, such as LLMs, to common-sense reasoning, formal reasoning, and downstream user interfaces such as chatbot assistants, AI teachers, and AI-curated social media. His 175+ papers have presented various algorithms for parsing, machine translation, and weighted finite-state machines; formalizations, algorithms, theorems, and empirical results in computational phonology; unsupervised and semi-supervised learning methods for syntax, morphology, and word-sense disambiguation; and principled methods for conversational AI, including language modeling, semantic parsing, reasoning, and evaluation. Eisner is also the lead designer of Dyna, a declarative programming language that provides an infrastructure for AI algorithms.