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short-paper

A Compare-Aggregate Model with Latent Clustering for Answer Selection

Authors:

Seunghyun Yoon,

Franck Dernoncourt,

Kyomin JungAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2093 - 2096

https://doi.org/10.1145/3357384.3358148

Published: 03 November 2019 Publication History

Abstract

In this paper, we propose a novel method for a sentence-level answer-selection task that is a fundamental problem in natural language processing. First, we explore the effect of additional information by adopting a pretrained language model to compute the vector representation of the input text and by applying transfer learning from a large-scale corpus. Second, we enhance the compare-aggregate model by proposing a novel latent clustering method to compute additional information within the target corpus and by changing the objective function from listwise to pointwise. To evaluate the performance of the proposed approaches, experiments are performed with the WikiQA and TREC-QA datasets. The empirical results demonstrate the superiority of our proposed approach, which achieve state-of-the-art performance for both datasets.

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Cited By

Jin XDu YWang BZhang Q(2024)Cross-biased Contrastive Learning for Answer Selection with Dual-Tower StructureNeurocomputing10.1016/j.neucom.2024.128641(128641)Online publication date: Sep-2024
https://doi.org/10.1016/j.neucom.2024.128641
Sadegh Zahedi MRahgozar MAghaeizadeh Zoroofi R(2024)MATER: Bi-level matching-aggregation model for time-aware expert recommendationExpert Systems with Applications10.1016/j.eswa.2023.121576237(121576)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121576
Li BYang PSun YHu ZYi M(2024)Multi-view pre-trained transformer via hierarchical capsule network for answer sentence selectionApplied Intelligence10.1007/s10489-024-05513-y54:21(10561-10580)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10489-024-05513-y
Show More Cited By

Index Terms

A Compare-Aggregate Model with Latent Clustering for Answer Selection
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering

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Published In

cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 03 November 2019

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CIKM '19

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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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Cited By

Jin XDu YWang BZhang Q(2024)Cross-biased Contrastive Learning for Answer Selection with Dual-Tower StructureNeurocomputing10.1016/j.neucom.2024.128641(128641)Online publication date: Sep-2024
https://doi.org/10.1016/j.neucom.2024.128641
Sadegh Zahedi MRahgozar MAghaeizadeh Zoroofi R(2024)MATER: Bi-level matching-aggregation model for time-aware expert recommendationExpert Systems with Applications10.1016/j.eswa.2023.121576237(121576)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121576
Li BYang PSun YHu ZYi M(2024)Multi-view pre-trained transformer via hierarchical capsule network for answer sentence selectionApplied Intelligence10.1007/s10489-024-05513-y54:21(10561-10580)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10489-024-05513-y
Xiao SDu SChen ZZhang YWang S(2023)Dual Fusion-Propagation Graph Neural Network for Multi-View ClusteringIEEE Transactions on Multimedia10.1109/TMM.2023.324817325(9203-9215)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2023.3248173
Higashi MSung MYamane DInamuro KNagai SKobayashi KNakata K(2023)Decision Tree Clustering for Time Series Data: An Approach for Enhanced Interpretability and EfficiencyPRICAI 2023: Trends in Artificial Intelligence10.1007/978-981-99-7022-3_42(457-468)Online publication date: 10-Nov-2023
https://doi.org/10.1007/978-981-99-7022-3_42
Wang WShen TBlumenstein MLong G(2023)Improving Open-Domain Answer Sentence Selection by Distributed Clients with Privacy PreservationAdvanced Data Mining and Applications10.1007/978-3-031-46677-9_2(15-29)Online publication date: 5-Nov-2023
https://doi.org/10.1007/978-3-031-46677-9_2
Nguyen MKishan KNguyen TChadha AVu T(2023)Efficient Fine-Tuning Large Language Models for Knowledge-Aware Response PlanningMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43415-0_35(593-611)Online publication date: 17-Sep-2023
https://doi.org/10.1007/978-3-031-43415-0_35
Sousa NOliveira NPraça I(2022)Machine Reading at Scale: A Search Engine for Scientific and Academic ResearchSystems10.3390/systems1002004310:2(43)Online publication date: 5-Apr-2022
https://doi.org/10.3390/systems10020043
Gharagozlou HMohammadzadeh JBastanfard AGhidary S(2022)RLAS-BIABCComputational Intelligence and Neuroscience10.1155/2022/78398402022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7839840
Bidgoly AAmirkhani HBaradaran R(2022)Clustering-based Sequence to Sequence Model for Generative Question Answering in a Low-resource LanguageACM Transactions on Asian and Low-Resource Language Information Processing10.1145/356303622:2(1-14)Online publication date: 27-Dec-2022
https://dl.acm.org/doi/10.1145/3563036
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