Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval
Fan Luo Mihai Surdeanu
Lexical and semantic matches are commonly used as relevance measurements for
information retrieval. Together they estimate the semantic equivalence between
the query and the candidates. However, semantic equivalence is not the only
relevance signal that needs to be considered when retrieving evidences for
multi-hop questions. In this work, we demonstrate that textual entailment
relation is another important relevance dimension that should be considered. To
retrieve evidences that are either semantically equivalent to or entailed by
the question simultaneously, we divide the task of evidence retrieval for
multi-hop question answering (QA) into two sub-tasks, i.e., semantic textual
similarity and inference similarity retrieval. We propose two ensemble models,
EAR and EARnest, which tackle each of the sub-tasks separately and then jointly
re-rank sentences with the consideration of the diverse relevance signals.
Experimental results on HotpotQA verify that our models not only significantly
outperform all the single retrieval models it is based on, but is also more
effective than two intuitive ensemble baseline models.