A Technical Question Answering System with Transfer Learning

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


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.
Anthology ID:
2020.emnlp-demos.13
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
October
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
92–99
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-demos.13
DOI:
10.18653/v1/2020.emnlp-demos.13
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-demos.13.pdf
Optional supplementary material:
 2020.emnlp-demos.13.OptionalSupplementaryMaterial.zip