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        <title>A Technical Question Answering System with Transfer Learning</title>
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    <name type="personal">
        <namePart type="given">Wenhao</namePart>
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        <namePart type="given">Lingfei</namePart>
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        <namePart type="given">Qingkai</namePart>
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            <namePart type="given">Qun</namePart>
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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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    <identifier type="doi">10.18653/v1/2020.emnlp-demos.13</identifier>
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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
%Y Liu, Qun
%Y Schlangen, David
%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)

• A Technical Question Answering System with Transfer Learning (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.