Technical Question Answering across Tasks and Domains

Wenhao Yu, Lingfei Wu, Yu Deng, Qingkai Zeng, Ruchi Mahindru, Sinem Guven, Meng Jiang


Abstract
Building automatic technical support system is an important yet challenge task. Conceptually, to answer a user question on a technical forum, a human expert has to first retrieve relevant documents, and then read them carefully to identify the answer snippet. Despite huge success the researchers have achieved in coping with general domain question answering (QA), much less attentions have been paid for investigating technical QA. Specifically, existing methods suffer from several unique challenges (i) the question and answer rarely overlaps substantially and (ii) very limited data size. In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains. To this end, we present an adjustable joint learning approach for document retrieval and reading comprehension tasks. Our experiments on the TechQA demonstrates superior performance compared with state-of-the-art methods.
Anthology ID:
2021.naacl-industry.23
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
178–186
Language:
URL:
https://aclanthology.org/2021.naacl-industry.23
DOI:
10.18653/v1/2021.naacl-industry.23
Bibkey:
Cite (ACL):
Wenhao Yu, Lingfei Wu, Yu Deng, Qingkai Zeng, Ruchi Mahindru, Sinem Guven, and Meng Jiang. 2021. Technical Question Answering across Tasks and Domains. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 178–186, Online. Association for Computational Linguistics.
Cite (Informal):
Technical Question Answering across Tasks and Domains (Yu et al., NAACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.naacl-industry.23.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2021.naacl-industry.23.mp4
Code
 wyu97/TransTD
Data
TechQA