What is the {J}eopardy Model? A Quasi-Synchronous Grammar for {QA

This paper presents a syntax-driven ap- proach to question answering, specifically the answer-sentence selection problem for short-answer questions. Rather than us- ing syntactic features to augment exist- ing statistical classifiers (as in previous work), we build on the idea that ques- tions and their (correct) answers relate to each other via loose but predictable syntac- tic transformations. We propose a prob- abilistic quasi-synchronous grammar, in- spired by one proposed for machine trans- lation (D. Smith and Eisner, 2006), and pa- rameterized by mixtures of a robust non- lexical syntax/alignment model with a(n optional) lexical-semantics-driven log-linear model. Our model learns soft alignments as a hidden variable in discriminative training. Experimentalresultsusing theTRECdataset are shown to significantly outperform strong state-of-the-art baselines.