تقرير
Technical Question Answering across Tasks and Domains
| العنوان: | Technical Question Answering across Tasks and Domains |
|---|---|
| المؤلفون: | Yu, Wenhao, Wu, Lingfei, Deng, Yu, Zeng, Qingkai, Mahindru, Ruchi, Guven, Sinem, Jiang, Meng |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: NAACL 2021 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2010.09780 |
| رقم الأكسشن: | edsarx.2010.09780 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |