@inproceedings{yu-etal-2020-crossing,
title = "Crossing Variational Autoencoders for Answer Retrieval",
author = "Yu, Wenhao and
Wu, Lingfei and
Zeng, Qingkai and
Tao, Shu and
Deng, Yu and
Jiang, Meng",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.498/",
doi = "10.18653/v1/2020.acl-main.498",
pages = "5635--5641",
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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<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.</abstract>
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%0 Conference Proceedings
%T Crossing Variational Autoencoders for Answer Retrieval
%A Yu, Wenhao
%A Wu, Lingfei
%A Zeng, Qingkai
%A Tao, Shu
%A Deng, Yu
%A Jiang, Meng
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F yu-etal-2020-crossing
%X 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.
%R 10.18653/v1/2020.acl-main.498
%U https://aclanthology.org/2020.acl-main.498/
%U https://doi.org/10.18653/v1/2020.acl-main.498
%P 5635-5641
Markdown (Informal)
[Crossing Variational Autoencoders for Answer Retrieval](https://aclanthology.org/2020.acl-main.498/) (Yu et al., ACL 2020)
ACL
- Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng, and Meng Jiang. 2020. Crossing Variational Autoencoders for Answer Retrieval. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5635–5641, Online. Association for Computational Linguistics.