Paper: Probabilistic Tree-Edit Models with Structured Latent Variables for Textual Entailment and Question Answering

ACL ID C10-1131
Title Probabilistic Tree-Edit Models with Structured Latent Variables for Textual Entailment and Question Answering
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

A range of Natural Language Process- ing tasks involve making judgments about the semantic relatedness of a pair of sen- tences, such as Recognizing Textual En- tailment (RTE) and answer selection for Question Answering (QA). A key chal- lenge that these tasks face in common is the lack of explicit alignment annota- tion between a sentence pair. We capture the alignment by using a novel probabilis- tic model that models tree-edit operations on dependency parse trees. Unlike previ- ous tree-edit models which require a sep- arate alignment-finding phase and resort to ad-hoc distance metrics, our method treats alignments as structured latent vari- ables, and offers a principled framework for incorporating complex linguistic fea- tures. We demonstrate the robustness of our model by conducting ...