A Technical Question Answering System with Transfer Learning

Autor: Qingkai Zeng, Sinem Guven, Ruchi Mahindru, Meng Jiang, Yu Deng, Lingfei Wu, Wenhao Yu
Rok vydání: 2020
Předmět:
Zdroj: EMNLP (Demos)
DOI: 10.18653/v1/2020.emnlp-demos.13
Popis: 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.
Databáze: OpenAIRE