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MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering
Progress in cross-lingual modeling depends on challenging, realistic, an...
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Towards Understanding and Answering Multi-Sentence Recommendation Questions on Tourism
We introduce the first system towards the novel task of answering comple...
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Table2answer: Read the database and answer without SQL
Semantic parsing is the task of mapping natural language to logic form. ...
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Identifying Well-formed Natural Language Questions
Understanding search queries is a hard problem as it involves dealing wi...
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Generating Semantically Valid Adversarial Questions for TableQA
Adversarial attack on question answering systems over tabular data (Tabl...
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How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions
We present a large-scale dataset for the task of rewriting an ill-formed...
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Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions
Modern natural language processing systems have been touted as approachi...
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Break It Down: A Question Understanding Benchmark
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through natural language, that are necessary for answering a question. We develop a crowdsourcing pipeline, showing that quality QDMRs can be annotated at scale, and release the Break dataset, containing over 83K pairs of questions and their QDMRs. We demonstrate the utility of QDMR by showing that (a) it can be used to improve open-domain question answering on the HotpotQA dataset, (b) it can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Last, we use Break to train a sequence-to-sequence model with copying that parses questions into QDMR structures, and show that it substantially outperforms several natural baselines.
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