@inproceedings{bourgonje-demberg-2024-generalizing,
title = "Generalizing across Languages and Domains for Discourse Relation Classification",
author = "Bourgonje, Peter and
Demberg, Vera",
editor = "Kawahara, Tatsuya and
Demberg, Vera and
Ultes, Stefan and
Inoue, Koji and
Mehri, Shikib and
Howcroft, David and
Komatani, Kazunori",
booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2024",
address = "Kyoto, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sigdial-1.47",
pages = "554--565",
abstract = "The availability of corpora annotated for discourse relations is limited and discourse relation classification performance varies greatly depending on both language and domain. This is a problem for downstream applications that are intended for a language (i.e., not English) or a domain (i.e., not financial news) with comparatively low coverage for discourse annotations. In this paper, we experiment with a state-of-the-art model for discourse relation classification, originally developed for English, extend it to a multi-lingual setting (testing on Italian, Portuguese and Turkish), and employ a simple, yet effective method to mark out-of-domain training instances. By doing so, we aim to contribute to better generalization and more robust discourse relation classification performance across both language and domain.",
}
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<abstract>The availability of corpora annotated for discourse relations is limited and discourse relation classification performance varies greatly depending on both language and domain. This is a problem for downstream applications that are intended for a language (i.e., not English) or a domain (i.e., not financial news) with comparatively low coverage for discourse annotations. In this paper, we experiment with a state-of-the-art model for discourse relation classification, originally developed for English, extend it to a multi-lingual setting (testing on Italian, Portuguese and Turkish), and employ a simple, yet effective method to mark out-of-domain training instances. By doing so, we aim to contribute to better generalization and more robust discourse relation classification performance across both language and domain.</abstract>
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%0 Conference Proceedings
%T Generalizing across Languages and Domains for Discourse Relation Classification
%A Bourgonje, Peter
%A Demberg, Vera
%Y Kawahara, Tatsuya
%Y Demberg, Vera
%Y Ultes, Stefan
%Y Inoue, Koji
%Y Mehri, Shikib
%Y Howcroft, David
%Y Komatani, Kazunori
%S Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F bourgonje-demberg-2024-generalizing
%X The availability of corpora annotated for discourse relations is limited and discourse relation classification performance varies greatly depending on both language and domain. This is a problem for downstream applications that are intended for a language (i.e., not English) or a domain (i.e., not financial news) with comparatively low coverage for discourse annotations. In this paper, we experiment with a state-of-the-art model for discourse relation classification, originally developed for English, extend it to a multi-lingual setting (testing on Italian, Portuguese and Turkish), and employ a simple, yet effective method to mark out-of-domain training instances. By doing so, we aim to contribute to better generalization and more robust discourse relation classification performance across both language and domain.
%U https://aclanthology.org/2024.sigdial-1.47
%P 554-565
Markdown (Informal)
[Generalizing across Languages and Domains for Discourse Relation Classification](https://aclanthology.org/2024.sigdial-1.47) (Bourgonje & Demberg, SIGDIAL 2024)
ACL