@inproceedings{metheniti-etal-2023-discut,
title = "{D}is{C}ut and {D}isc{R}e{T}: {MELODI} at {DISRPT} 2023",
author = "Metheniti, Eleni and
Braud, Chlo{\'e} and
Muller, Philippe and
Rivi{\`e}re, Laura",
editor = "Braud, Chlo{\'e} and
Liu, Yang Janet and
Metheniti, Eleni and
Muller, Philippe and
Rivi{\`e}re, Laura and
Rutherford, Attapol and
Zeldes, Amir",
booktitle = "Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "The Association for Computational Linguistics",
url = "https://aclanthology.org/2023.disrpt-1.3",
doi = "10.18653/v1/2023.disrpt-1.3",
pages = "29--42",
abstract = "This paper presents the results obtained by the MELODI team for the three tasks proposed within the DISRPT 2023 shared task on discourse: segmentation, connective identification, and relation classification. The competition involves corpora in various languages in several underlying frameworks, and proposes two tracks depending on the presence or not of annotations of sentence boundaries and syntactic information. For these three tasks, we rely on a transformer-based architecture, and investigate several optimizations of the models, including hyper-parameter search and layer freezing. For discourse relations, we also explore the use of adapters{---}a lightweight solution for model fine-tuning{---}and introduce relation mappings to partially deal with the label set explosion we are facing within the setting of the shared task in a multi-corpus perspective. In the end, we propose one single architecture for segmentation and connectives, based on XLM-RoBERTa large, freezed at lower layers, with new state-of-the-art results for segmentation, and we propose 3 different models for relations, since the task makes it harder to generalize across all corpora.",
}
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<abstract>This paper presents the results obtained by the MELODI team for the three tasks proposed within the DISRPT 2023 shared task on discourse: segmentation, connective identification, and relation classification. The competition involves corpora in various languages in several underlying frameworks, and proposes two tracks depending on the presence or not of annotations of sentence boundaries and syntactic information. For these three tasks, we rely on a transformer-based architecture, and investigate several optimizations of the models, including hyper-parameter search and layer freezing. For discourse relations, we also explore the use of adapters—a lightweight solution for model fine-tuning—and introduce relation mappings to partially deal with the label set explosion we are facing within the setting of the shared task in a multi-corpus perspective. In the end, we propose one single architecture for segmentation and connectives, based on XLM-RoBERTa large, freezed at lower layers, with new state-of-the-art results for segmentation, and we propose 3 different models for relations, since the task makes it harder to generalize across all corpora.</abstract>
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%0 Conference Proceedings
%T DisCut and DiscReT: MELODI at DISRPT 2023
%A Metheniti, Eleni
%A Braud, Chloé
%A Muller, Philippe
%A Rivière, Laura
%Y Braud, Chloé
%Y Liu, Yang Janet
%Y Metheniti, Eleni
%Y Muller, Philippe
%Y Rivière, Laura
%Y Rutherford, Attapol
%Y Zeldes, Amir
%S Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023)
%D 2023
%8 July
%I The Association for Computational Linguistics
%C Toronto, Canada
%F metheniti-etal-2023-discut
%X This paper presents the results obtained by the MELODI team for the three tasks proposed within the DISRPT 2023 shared task on discourse: segmentation, connective identification, and relation classification. The competition involves corpora in various languages in several underlying frameworks, and proposes two tracks depending on the presence or not of annotations of sentence boundaries and syntactic information. For these three tasks, we rely on a transformer-based architecture, and investigate several optimizations of the models, including hyper-parameter search and layer freezing. For discourse relations, we also explore the use of adapters—a lightweight solution for model fine-tuning—and introduce relation mappings to partially deal with the label set explosion we are facing within the setting of the shared task in a multi-corpus perspective. In the end, we propose one single architecture for segmentation and connectives, based on XLM-RoBERTa large, freezed at lower layers, with new state-of-the-art results for segmentation, and we propose 3 different models for relations, since the task makes it harder to generalize across all corpora.
%R 10.18653/v1/2023.disrpt-1.3
%U https://aclanthology.org/2023.disrpt-1.3
%U https://doi.org/10.18653/v1/2023.disrpt-1.3
%P 29-42
Markdown (Informal)
[DisCut and DiscReT: MELODI at DISRPT 2023](https://aclanthology.org/2023.disrpt-1.3) (Metheniti et al., DISRPT 2023)
ACL
- Eleni Metheniti, Chloé Braud, Philippe Muller, and Laura Rivière. 2023. DisCut and DiscReT: MELODI at DISRPT 2023. In Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023), pages 29–42, Toronto, Canada. The Association for Computational Linguistics.