HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering

01/12/2019
by   Liang Pang, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/12/2018

Training a Ranking Function for Open-Domain Question Answering

In recent years, there have been amazing advances in deep learning metho...
research
01/23/2018

Assertion-based QA with Question-Aware Open Information Extraction

We present assertion based question answering (ABQA), an open domain que...
research
10/22/2021

ListReader: Extracting List-form Answers for Opinion Questions

Question answering (QA) is a high-level ability of natural language proc...
research
09/29/2020

MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension

Span extraction is an essential problem in machine reading comprehension...
research
08/28/2020

Rethinking the objectives of extractive question answering

This paper describes two generally applicable approaches towards the sig...
research
10/21/2020

RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering

State-of-the-art Machine Reading Comprehension (MRC) models for Open-dom...
research
11/29/2022

Which Shortcut Solution Do Question Answering Models Prefer to Learn?

Question answering (QA) models for reading comprehension tend to learn s...

Please sign up or login with your details

Forgot password? Click here to reset