Paper Info
Title
HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering.
Abstract
This paper is concerned with open-domain question answering (i.e., OpenQA). Recently, some works have viewed this problem as a reading comprehension (RC) task, and directly applied successful RC models to it. However, the performances of such models are not so good as that in the RC task. In our opinion, the perspective of RC ignores three characteristics in OpenQA task: 1) many paragraphs without the answer span are included in the data collection; 2) multiple answer spans may exist within one given paragraph; 3) the end position of an answer span is dependent with the start position. In this paper, we first propose a new probabilistic formulation of OpenQA, based on a three-level hierarchical structure, i.e., the question level, the paragraph level and the answer span level. Then a Hierarchical Answer Spans Model (HAS-QA) is designed to capture each probability. HAS-QA has the ability to tackle the above three problems, and experiments on public OpenQA datasets show that it significantly outperforms traditional RC baselines and recent OpenQA baselines.
Year
Venue
Field
2019
THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
Data collection,Question answering,Computer science,Reading comprehension,Paragraph,Natural language processing,Artificial intelligence,Probabilistic logic
DocType
Volume
Citations 
Journal
abs/1901.03866
1
PageRank 
References 
Authors
0.35
0
6
Name
Order
Citations
PageRank
Liang Pang123119.39
Yanyan Lan2100563.59
Jiafeng Guo31737102.17
Jun Xu4143574.49
Lixin Su522.06
Xueqi Cheng63148247.04