Evidence aggregation for answer re-ranking in open-domain question answering
A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evide...
Saved in:
Main Authors: | , , , , , , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2018
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/4238 https://ink.library.smu.edu.sg/context/sis_research/article/5241/viewcontent/171105116.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-5241 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-52412019-01-17T06:03:51Z Evidence aggregation for answer re-ranking in open-domain question answering WANG, Shuohang YU, Mo JIANG, Jing ZHANG, Wei GUO, Xiaoxiao CHANG, Shiyu WANG, Zhiguo KLINGER, Tim TESAURO, Gerald CAMPBELL, Murray A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer. Our models have achieved state-of-the-art results on three public open-domain QA datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8 percentage points of improvement over the former two datasets. 2018-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4238 https://ink.library.smu.edu.sg/context/sis_research/article/5241/viewcontent/171105116.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Databases and Information Systems |
spellingShingle |
Databases and Information Systems WANG, Shuohang YU, Mo JIANG, Jing ZHANG, Wei GUO, Xiaoxiao CHANG, Shiyu WANG, Zhiguo KLINGER, Tim TESAURO, Gerald CAMPBELL, Murray Evidence aggregation for answer re-ranking in open-domain question answering |
description |
A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer. Our models have achieved state-of-the-art results on three public open-domain QA datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8 percentage points of improvement over the former two datasets. |
format |
text |
author |
WANG, Shuohang YU, Mo JIANG, Jing ZHANG, Wei GUO, Xiaoxiao CHANG, Shiyu WANG, Zhiguo KLINGER, Tim TESAURO, Gerald CAMPBELL, Murray |
author_facet |
WANG, Shuohang YU, Mo JIANG, Jing ZHANG, Wei GUO, Xiaoxiao CHANG, Shiyu WANG, Zhiguo KLINGER, Tim TESAURO, Gerald CAMPBELL, Murray |
author_sort |
WANG, Shuohang |
title |
Evidence aggregation for answer re-ranking in open-domain question answering |
title_short |
Evidence aggregation for answer re-ranking in open-domain question answering |
title_full |
Evidence aggregation for answer re-ranking in open-domain question answering |
title_fullStr |
Evidence aggregation for answer re-ranking in open-domain question answering |
title_full_unstemmed |
Evidence aggregation for answer re-ranking in open-domain question answering |
title_sort |
evidence aggregation for answer re-ranking in open-domain question answering |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2018 |
url |
https://ink.library.smu.edu.sg/sis_research/4238 https://ink.library.smu.edu.sg/context/sis_research/article/5241/viewcontent/171105116.pdf |
_version_ |
1770574497935523840 |