@inproceedings{liu-etal-2023-hits,
title = "{HITS} at {DISRPT} 2023: Discourse Segmentation, Connective Detection, and Relation Classification",
author = "Liu, Wei and
Fan, Yi and
Strube, Michael",
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-1.4",
doi = "10.18653/v1/2023.disrpt-1.4",
pages = "43--49",
abstract = "HITS participated in the Discourse Segmentation (DS, Task 1) and Connective Detection (CD, Task 2) tasks at the DISRPT 2023. Task 1 focuses on segmenting the text into discourse units, while Task 2 aims to detect the discourse connectives. We deployed a framework based on different pre-trained models according to the target language for these two tasks.HITS also participated in the Relation Classification track (Task 3). The main task was recognizing the discourse relation between text spans from different languages. We designed a joint model for languages with a small corpus while separate models for large corpora. The adversarial training strategy is applied to enhance the robustness of relation classifiers.",
}
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<abstract>HITS participated in the Discourse Segmentation (DS, Task 1) and Connective Detection (CD, Task 2) tasks at the DISRPT 2023. Task 1 focuses on segmenting the text into discourse units, while Task 2 aims to detect the discourse connectives. We deployed a framework based on different pre-trained models according to the target language for these two tasks.HITS also participated in the Relation Classification track (Task 3). The main task was recognizing the discourse relation between text spans from different languages. We designed a joint model for languages with a small corpus while separate models for large corpora. The adversarial training strategy is applied to enhance the robustness of relation classifiers.</abstract>
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%0 Conference Proceedings
%T HITS at DISRPT 2023: Discourse Segmentation, Connective Detection, and Relation Classification
%A Liu, Wei
%A Fan, Yi
%A Strube, Michael
%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 liu-etal-2023-hits
%X HITS participated in the Discourse Segmentation (DS, Task 1) and Connective Detection (CD, Task 2) tasks at the DISRPT 2023. Task 1 focuses on segmenting the text into discourse units, while Task 2 aims to detect the discourse connectives. We deployed a framework based on different pre-trained models according to the target language for these two tasks.HITS also participated in the Relation Classification track (Task 3). The main task was recognizing the discourse relation between text spans from different languages. We designed a joint model for languages with a small corpus while separate models for large corpora. The adversarial training strategy is applied to enhance the robustness of relation classifiers.
%R 10.18653/v1/2023.disrpt-1.4
%U https://aclanthology.org/2023.disrpt-1.4
%U https://doi.org/10.18653/v1/2023.disrpt-1.4
%P 43-49
Markdown (Informal)
[HITS at DISRPT 2023: Discourse Segmentation, Connective Detection, and Relation Classification](https://aclanthology.org/2023.disrpt-1.4) (Liu et al., DISRPT 2023)
ACL