A Noisy-Channel Approach to Question Answering

A Noisy-Channel Approach to Question Answering Abdessamad Echihabi and Daniel Marcu Information Sciences Institute Department of Computer Science University of Southern California 4676 Admiralty Way, Suite 1001 Marina Del Rey, CA 90292 {echihabi,marcu}@isi.edu Abstract We introduce a probabilistic noisy- channel model for question answering and we show how it can be exploited in the context of an end-to-end QA system. Our noisy-channel system outperforms a state- of-the-art rule-based QA system that uses similar resources. We also show that the model we propose is flexible enough to accommodate within one mathematical framework many QA-specific resources and techniques, which range from the exploitation of WordNet, structured, and semi-structured databases to reasoning, and paraphrasing. 1 Introduction Current state-of-the-art Question Answering (QA) systems are extremely complex. They contain tens of modules that do everything from information retrieval, sentence parsing (Ittycheriah and Roukos, 2002; Hovy et al., 2001; Moldovan et al, 2002), question-type pinpointing (Ittycheriah and Roukos, 2002; Hovy et al., 2001; Moldovan et al, 2002), semantic analysis (Xu et al., Hovy et al., 2001; Moldovan et al, 2002), and reasoning (Moldovan et al, 2002). They access external resources such as the WordNet (Hovy et al., 2001, Pasca and Harabagiu, 2001, Prager et al., 2001), the web (Brill et al., 2001), structured, and semi- structured databases (Katz et al., 2001; Lin, 2002; Clarke, 2001). They contain feedback loops, ranking, and re-ranking modules. Given their complexity, it is often difficult (and sometimes impossible) to understand what contributes to the performance of a system and what doesn’t. In this paper, we propose a new approach to QA in which the contribution of various resources and components can be easily assessed. The fundamental insight of our approach, which departs significantly from the current architectures, is that, at its core, a QA system is a pipeline of only two modules: • An IR engine that retrieves a set of M documents/N sentences that may contain answers to a given question Q. • And an answer identifier module that given a question Q and a sentence S (from the set of sentences retrieved by the IR engine) identifies a sub-string SA of S that is likely to be an answer to Q and assigns a score to it. Once one has these two modules, one has a QA system because finding the answer to a question Q amounts to selecting the sub-string SA of highest score. Although this view is not made explicit by QA researchers, it is implicitly present in all systems we are aware of. In its simplest form, if one accepts a whole sentence as an answer (SA = S), one can assess the likelihood that a sentence S contains the answer to a question Q by measuring the cosine similarity between Q and S. However, as research in QA demonstrates, word-overlap is not a good enough metric for determining whether a sentence contains the answer to a question. Consider, for example, the question “Who is the leader of France?” The sentence “Henri Hadjenberg, who is the …

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