An Entailment-Based Approach to the QA4MRE Challenge
Peter E. Clark, Phil Harrison, Xuchen Yao · 2012
Abstract. This paper describes our entry to the 2012 QA4MRE Main Task (English dataset). The QA4MRE task poses a significant challenge as the ex-pression of knowledge in the question and answer (in the document) typically substantially differs. Ultimately, one would need a system that can perform full machine reading – creating an internal model of the document’s meaning – to achieve high performance. Our approach is a preliminary step toward this, based on estimating the likelihood of textual entailment between sentences in the text, and the question Q and each candidate answer Ai. We first treat the question Q and each answer Ai independently, and find sets of sentences SQ, SA that each plausibly entail (the target of) Q or one of the Ai respectively. We then search for the closest (in the document) pair of sentences in these sets, and conclude that the answer Ai entailed by SAi in the closest pair is the answer. This approach assumes coherent discourse, i.e., that sentences close together are usually “talking about the same thing”, and thus conveying a single idea (namely an expression of the Q+Ai pair).