Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AF4NB9QQD" target="_blank" >RIV/00216208:11320/23:F4NB9QQD - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2023.findings-acl.135/" target="_blank" >https://aclanthology.org/2023.findings-acl.135/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2023.findings-acl.135" target="_blank" >10.18653/v1/2023.findings-acl.135</a>

Result language

angličtina

Original language name

Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection

Original language description

"We study the problem of cross-lingual transfer learning for event detection (ED) where models trained on a source language are expected to perform well on data for a new target language. Among a few recent works for this problem, the main approaches involve representation matching (e.g., adversarial training) that aims to eliminate language-specific features from the representations to achieve the language-invariant representations. However, due to the mix of language-specific features with event-discriminative context, representation matching methods might also remove important features for event prediction, thus hindering the performance for ED. To address this issue, we introduce a novel approach for cross-lingual ED where representations are augmented with additional context (i.e., not eliminating) to bridge the gap between languages while enriching the contextual information to facilitate ED. At the core of our method involves a retrieval model that retrieves relevant sentences in the target language for an input sentence to compute augmentation representations. Experiments on three languages demonstrate the state-of-the-art performance of our model for cross-lingual ED."

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

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Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

"Findings of the Association for Computational Linguistics: ACL 2023"

ISBN

978-1-959429-62-3

ISSN

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e-ISSN

—

Number of pages

14

Pages from-to

2157-2170

Publisher name

ACL

Place of publication

Toronto, Canada

Event location

Toronto, Canada

Event date

Jan 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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