Abstract
We propose a robust answer reranking model for non-factoid questions that integrates lexical semantics with discourse information, driven by two representations of discourse: a shallow representation centered around discourse markers, and a deep one based on Rhetorical Structure Theory. We evaluate the proposed model on two corpora from different genres and domains: one from Yahoo! Answers and one from the biology domain, and two types of non-factoid questions: manner and reason. We experimentally demonstrate that the discourse structure of nonfactoid answers provides information that is complementary to lexical semantic similarity between question and answer, improving performance up to 24% (relative) over a state-of-the-art model that exploits lexical semantic similarity alone. We further demonstrate excellent domain transfer of discourse information, suggesting these discourse features have general utility to non-factoid question answering.
Highlights
Driven by several international evaluations and workshops such as the Text REtrieval Conference (TREC)1 and the Cross Language Evaluation Forum (CLEF),2 the task of question answering (QA) has received considerable attention
We propose a novel answer reranking (AR) model that combines lexical semantics (LS) with discourse information, driven by two representations of discourse: a shallow representation centered around discourse markers and surface text information, and a deep one based on the Rhetorical Structure Theory (RST) discourse framework (Mann and Thompson, 1988)
The contributions of this work are: 1. We demonstrate that modeling discourse is greatly beneficial for NF AR for two types of NF questions, manner (“how”) and reason (“why”), across two large datasets from different genres and domains – one from the community question-answering (CQA) site of Yahoo! Answers3, and one from a biology textbook
Summary
Driven by several international evaluations and workshops such as the Text REtrieval Conference (TREC) and the Cross Language Evaluation Forum (CLEF), the task of question answering (QA) has received considerable attention. The vast majority of QA models explore only local linguistic structures, such as syntactic dependencies or semantic role frames, 1http://trec.nist.gov 2http://www.clef-initiative.eu which are generally restricted to individual sentences. This is problematic for NF QA, where questions are answered not by atomic facts, but by larger cross-sentence conceptual structures that convey the desired answers. Our results show statistically significant improvements of up to 24% on top of state-of-the-art LS models (Yih et al, 2013)
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