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How to answer inference based questions in RC? 

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Journal ArticleDOI
01 Jul 2012
17 Citations
Automated reasoning can potentially be used to answer these questions.
We demonstrate the benefits of our approach for answer sentence selection, which is a well-known inference task in Question Answering.
In this paper, we propose an effective and interpretable Select, Answer and Explain (SAE) system to solve the multi-document RC problem.
We propose decoupling KB-based inference by transforming a question into a high-level triplet in the KB, which makes it possible to apply KB-based inference methods to answer complex questions.
We present QuAIL, the first RC dataset to combine text-based, world knowledge and unanswerable questions, and to provide question type annotation that would enable diagnostics of the reasoning strategies by a given QA system.
Furthermore, we improve the existing answer inference methods and derive the correct result in an efficient way.
The proposed method decouples the language-based concept discovery and vision-based concept verification in the process of answer inference to prevent language priors from dominating the answering process.
Subjects were as fast and accurate to inference questions from the read perspective as from the new perspective, suggesting that inference questions are verified against a representation of the situation described by the text.

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