@inproceedings{metheniti-etal-2024-feature,
title = "Feature-augmented model for multilingual discourse relation classification",
author = "Metheniti, Eleni and
Braud, Chlo{\'e} and
Muller, Philippe",
editor = "Strube, Michael and
Braud, Chloe and
Hardmeier, Christian and
Li, Junyi Jessy and
Loaiciga, Sharid and
Zeldes, Amir and
Li, Chuyuan",
booktitle = "Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.codi-1.9",
pages = "91--104",
abstract = "Discourse relation classification within a multilingual, cross-framework setting is a challenging task, and the best-performing systems so far have relied on monolingual and mono-framework approaches.In this paper, we introduce transformer-based multilingual models, trained jointly over all datasets{---}thus covering different languages and discourse frameworks. We demonstrate their ability to outperform single-corpus models and to overcome (to some extent) the disparity among corpora, by relying on linguistic features and generic information about the nature of the datasets. We also compare the performance of different multilingual pretrained models, as well as the encoding of the relation direction, a key component for the task. Our results on the 16 datasets of the DISRPT 2021 benchmark show improvements in accuracy in (almost) all datasets compared to the monolingual models, with at best 65.91{\%} in average accuracy, thus corresponding to a 4{\%} improvement over the state-of-the-art.",
}
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%0 Conference Proceedings
%T Feature-augmented model for multilingual discourse relation classification
%A Metheniti, Eleni
%A Braud, Chloé
%A Muller, Philippe
%Y Strube, Michael
%Y Braud, Chloe
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Loaiciga, Sharid
%Y Zeldes, Amir
%Y Li, Chuyuan
%S Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F metheniti-etal-2024-feature
%X Discourse relation classification within a multilingual, cross-framework setting is a challenging task, and the best-performing systems so far have relied on monolingual and mono-framework approaches.In this paper, we introduce transformer-based multilingual models, trained jointly over all datasets—thus covering different languages and discourse frameworks. We demonstrate their ability to outperform single-corpus models and to overcome (to some extent) the disparity among corpora, by relying on linguistic features and generic information about the nature of the datasets. We also compare the performance of different multilingual pretrained models, as well as the encoding of the relation direction, a key component for the task. Our results on the 16 datasets of the DISRPT 2021 benchmark show improvements in accuracy in (almost) all datasets compared to the monolingual models, with at best 65.91% in average accuracy, thus corresponding to a 4% improvement over the state-of-the-art.
%U https://aclanthology.org/2024.codi-1.9
%P 91-104
Markdown (Informal)
[Feature-augmented model for multilingual discourse relation classification](https://aclanthology.org/2024.codi-1.9) (Metheniti et al., CODI-WS 2024)
ACL