Crossing Variational Autoencoders for Answer Retrieval

• URL: http://arxiv.org/abs/2005.02557v2
• Date: Mon, 6 Jul 2020 03:24:23 GMT
• Title: Crossing Variational Autoencoders for Answer Retrieval
• Authors: Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng, Meng Jiang
• Abstract summary: Question-answer alignment and question/answer semantics are two important signals for learning the representations. We propose to cross variational auto-encoders by generating questions with aligned answers and generating answers with aligned questions.
• Score: 50.17311961755684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Answer retrieval is to find the most aligned answer from a large set of candidates given a question. Learning vector representations of questions/answers is the key factor. Question-answer alignment and question/answer semantics are two important signals for learning the representations. Existing methods learned semantic representations with dual encoders or dual variational auto-encoders. The semantic information was learned from language models or question-to-question (answer-to-answer) generative processes. However, the alignment and semantics were too separate to capture the aligned semantics between question and answer. In this work, we propose to cross variational auto-encoders by generating questions with aligned answers and generating answers with aligned questions. Experiments show that our method outperforms the state-of-the-art answer retrieval method on SQuAD.

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