Answering Naturally: Factoid to Full length Answer Generation
Vaishali Pal, Manish Shrivastava, Irshad Bhat
Abstract
In recent years, the task of Question Answering over passages, also pitched as a reading comprehension, has evolved into a very active research area. A reading comprehension system extracts a span of text, comprising of named entities, dates, small phrases, etc., which serve as the answer to a given question. However, these spans of text would result in an unnatural reading experience in a conversational system. Usually, dialogue systems solve this issue by using template-based language generation. These systems, though adequate for a domain specific task, are too restrictive and predefined for a domain independent system. In order to present the user with a more conversational experience, we propose a pointer generator based full-length answer generator which can be used with most QA systems. Our system generates a full length answer given a question and the extracted factoid/span answer without relying on the passage from where the answer was extracted. We also present a dataset of 315000 question, factoid answer and full length answer triples. We have evaluated our system using ROUGE-1,2,L and BLEU and achieved 74.05 BLEU score and 86.25 Rogue-L score.- Anthology ID:
- D19-5401
- Volume:
- Proceedings of the 2nd Workshop on New Frontiers in Summarization
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venues:
- EMNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–9
- Language:
- URL:
- https://www.aclweb.org/anthology/D19-5401
- DOI:
- 10.18653/v1/D19-5401
- PDF:
- http://aclanthology.lst.uni-saarland.de/D19-5401.pdf