Abstract
Multi-text applications, such as multidocument summarization, are typically required to model redundancies across related texts. Current methods confronting consolidation struggle to fuse overlapping information. In order to explicitly represent content overlap, we propose to align predicate-argument relations across texts, providing a potential scaffold for information consolidation. We go beyond clustering coreferring mentions, and instead model overlap with respect to redundancy at a propositional level, rather than merely detecting shared referents. Our setting exploits QA-SRL, utilizing question-answer pairs to capture predicate-argument relations, facilitating laymen annotation of cross-text alignments. We employ crowd-workers for constructing a dataset of QA-based alignments, and present a baseline QA alignment model trained over our dataset. Analyses show that our new task is semantically challenging, capturing content overlap beyond lexical similarity and complements cross-document coreference with proposition-level links, offering potential use for downstream tasks.
Original language | English |
---|---|
Title of host publication | EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 9879-9894 |
Number of pages | 16 |
ISBN (Electronic) | 9781955917094 |
State | Published - 2021 |
Event | 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Virtual, Punta Cana, Dominican Republic Duration: 7 Nov 2021 → 11 Nov 2021 |
Publication series
Name | EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings |
---|
Conference
Conference | 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 |
---|---|
Country/Territory | Dominican Republic |
City | Virtual, Punta Cana |
Period | 7/11/21 → 11/11/21 |
Bibliographical note
Publisher Copyright:© 2021 Association for Computational Linguistics
Funding
We would like to thank the anonymous reviewers for their thorough and insightful comments. The work described herein was supported in part by grants from Intel Labs, Facebook, and the Israel Science Foundation grant 1951/17.
Funders | Funder number |
---|---|
Intel Labs | |
Israel Science Foundation | 1951/17 |