Trained trigger language model for sentence retrieval in QA

Abstract

We propose a novel language model for sentence retrieval in Question Answering (QA) systems called trained trigger language model. This model addresses the word mismatch problem in information retrieval. The proposed model captures pairs of trigger and target words while training on a large corpus. The word pairs are extracted based on both unsupervised and supervised approaches while different notions of triggering are used. In addition, we study the impact of corpus size and domain for a supervised model. All notions of the trained trigger model are finally used in a language model-based sentence retrieval framework. Our experiments on TREC QA collection verify that the proposed model significantly improves the sentence retrieval performance compared to the state-of-the-art translation model and class model which address the same problem.