@inproceedings{kamaladdini-ezzabady-etal-2021-multi,
title = "Multi-lingual Discourse Segmentation and Connective Identification: {MELODI} at Disrpt2021",
author = "Kamaladdini Ezzabady, Morteza and
Muller, Philippe and
Braud, Chlo{\'e}",
editor = "Zeldes, Amir and
Liu, Yang Janet and
Iruskieta, Mikel and
Muller, Philippe and
Braud, Chlo{\'e} and
Badene, Sonia",
booktitle = "Proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021)",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.disrpt-1.3",
doi = "10.18653/v1/2021.disrpt-1.3",
pages = "22--32",
abstract = "We present an approach for discourse segmentation and discourse connective identification, both at the sentence and document level, within the Disrpt 2021 shared task, a multi-lingual and multi-formalism evaluation campaign. Building on the most successful architecture from the 2019 similar shared task, we leverage datasets in the same or similar languages to augment training data and improve on the best systems from the previous campaign on 3 out of 4 subtasks, with a mean improvement on all 16 datasets of 0.85{\%}. Within the Disrpt 21 campaign the system ranks 3rd overall, very close to the 2nd system, but with a significant gap with respect to the best system, which uses a rich set of additional features. The system is nonetheless the best on languages that benefited from crosslingual training on sentence internal segmentation (German and Spanish).",
}
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%0 Conference Proceedings
%T Multi-lingual Discourse Segmentation and Connective Identification: MELODI at Disrpt2021
%A Kamaladdini Ezzabady, Morteza
%A Muller, Philippe
%A Braud, Chloé
%Y Zeldes, Amir
%Y Liu, Yang Janet
%Y Iruskieta, Mikel
%Y Muller, Philippe
%Y Braud, Chloé
%Y Badene, Sonia
%S Proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F kamaladdini-ezzabady-etal-2021-multi
%X We present an approach for discourse segmentation and discourse connective identification, both at the sentence and document level, within the Disrpt 2021 shared task, a multi-lingual and multi-formalism evaluation campaign. Building on the most successful architecture from the 2019 similar shared task, we leverage datasets in the same or similar languages to augment training data and improve on the best systems from the previous campaign on 3 out of 4 subtasks, with a mean improvement on all 16 datasets of 0.85%. Within the Disrpt 21 campaign the system ranks 3rd overall, very close to the 2nd system, but with a significant gap with respect to the best system, which uses a rich set of additional features. The system is nonetheless the best on languages that benefited from crosslingual training on sentence internal segmentation (German and Spanish).
%R 10.18653/v1/2021.disrpt-1.3
%U https://aclanthology.org/2021.disrpt-1.3
%U https://doi.org/10.18653/v1/2021.disrpt-1.3
%P 22-32
Markdown (Informal)
[Multi-lingual Discourse Segmentation and Connective Identification: MELODI at Disrpt2021](https://aclanthology.org/2021.disrpt-1.3) (Kamaladdini Ezzabady et al., DISRPT 2021)
ACL