@inproceedings{yu-etal-2020-technical,
title = "A Technical Question Answering System with Transfer Learning",
author = "Yu, Wenhao and
Wu, Lingfei and
Deng, Yu and
Mahindru, Ruchi and
Zeng, Qingkai and
Guven, Sinem and
Jiang, Meng",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-demos.13",
doi = "10.18653/v1/2020.emnlp-demos.13",
pages = "92--99",
abstract = "In recent years, the need for community technical question-answering sites has increased significantly. However, it is often expensive for human experts to provide timely and helpful responses on those forums. We develop TransTQA, which is a novel system that offers automatic responses by retrieving proper answers based on correctly answered similar questions in the past. TransTQA is built upon a siamese ALBERT network, which enables it to respond quickly and accurately. Furthermore, TransTQA adopts a standard deep transfer learning strategy to improve its capability of supporting multiple technical domains.",
}
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<abstract>In recent years, the need for community technical question-answering sites has increased significantly. However, it is often expensive for human experts to provide timely and helpful responses on those forums. We develop TransTQA, which is a novel system that offers automatic responses by retrieving proper answers based on correctly answered similar questions in the past. TransTQA is built upon a siamese ALBERT network, which enables it to respond quickly and accurately. Furthermore, TransTQA adopts a standard deep transfer learning strategy to improve its capability of supporting multiple technical domains.</abstract>
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%0 Conference Proceedings
%T A Technical Question Answering System with Transfer Learning
%A Yu, Wenhao
%A Wu, Lingfei
%A Deng, Yu
%A Mahindru, Ruchi
%A Zeng, Qingkai
%A Guven, Sinem
%A Jiang, Meng
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2020
%8 October
%I Association for Computational Linguistics
%C Online
%F yu-etal-2020-technical
%X In recent years, the need for community technical question-answering sites has increased significantly. However, it is often expensive for human experts to provide timely and helpful responses on those forums. We develop TransTQA, which is a novel system that offers automatic responses by retrieving proper answers based on correctly answered similar questions in the past. TransTQA is built upon a siamese ALBERT network, which enables it to respond quickly and accurately. Furthermore, TransTQA adopts a standard deep transfer learning strategy to improve its capability of supporting multiple technical domains.
%R 10.18653/v1/2020.emnlp-demos.13
%U https://aclanthology.org/2020.emnlp-demos.13
%U https://doi.org/10.18653/v1/2020.emnlp-demos.13
%P 92-99
Markdown (Informal)
[A Technical Question Answering System with Transfer Learning](https://aclanthology.org/2020.emnlp-demos.13) (Yu et al., EMNLP 2020)
ACL
- Wenhao Yu, Lingfei Wu, Yu Deng, Ruchi Mahindru, Qingkai Zeng, Sinem Guven, and Meng Jiang. 2020. A Technical Question Answering System with Transfer Learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 92–99, Online. Association for Computational Linguistics.