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[4Q2-GS-9-01] Open-Domain Question-Answering using NLG based query expansion and ranking method
Keywords:Open-Domain Question Answering, Information Retrieval, Machine Reading Comprehension
Open-Domain Question-Answering is the combined task of machine reading comprehension and information retrieval. Unlike general Question-Answering, Open-Domain Question-Answering lacks context data that contains the answer of the question, so it requires retrieving context candidates. To solve this task, we propose 3 approaches. 1.Query expansion from reading comprehension dataset, 2.Normalize reading comprehension output via sigmoid function, 3. Ranking and merging score with a threshold. In our experiment with Japanese Question-Answering dataset without context, our approach improves exact match score over previous method.
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