@inproceedings{gessler-etal-2021-discodisco,
title = "{D}is{C}o{D}is{C}o at the {DISRPT}2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection",
author = "Gessler, Luke and
Behzad, Shabnam and
Liu, Yang Janet and
Peng, Siyao and
Zhu, Yilun and
Zeldes, Amir",
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-1.6",
doi = "10.18653/v1/2021.disrpt-1.6",
pages = "51--62",
abstract = "This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. Our system, called DisCoDisCo, is a Transformer-based neural classifier which enhances contextualized word embeddings (CWEs) with hand-crafted features, relying on tokenwise sequence tagging for discourse segmentation and connective detection, and a feature-rich, encoder-less sentence pair classifier for relation classification. Our results for the first two tasks outperform SOTA scores from the previous 2019 shared task, and results on relation classification suggest strong performance on the new 2021 benchmark. Ablation tests show that including features beyond CWEs are helpful for both tasks, and a partial evaluation of multiple pretrained Transformer-based language models indicates that models pre-trained on the Next Sentence Prediction (NSP) task are optimal for relation classification.",
}
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<abstract>This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. Our system, called DisCoDisCo, is a Transformer-based neural classifier which enhances contextualized word embeddings (CWEs) with hand-crafted features, relying on tokenwise sequence tagging for discourse segmentation and connective detection, and a feature-rich, encoder-less sentence pair classifier for relation classification. Our results for the first two tasks outperform SOTA scores from the previous 2019 shared task, and results on relation classification suggest strong performance on the new 2021 benchmark. Ablation tests show that including features beyond CWEs are helpful for both tasks, and a partial evaluation of multiple pretrained Transformer-based language models indicates that models pre-trained on the Next Sentence Prediction (NSP) task are optimal for relation classification.</abstract>
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%0 Conference Proceedings
%T DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection
%A Gessler, Luke
%A Behzad, Shabnam
%A Liu, Yang Janet
%A Peng, Siyao
%A Zhu, Yilun
%A Zeldes, Amir
%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 gessler-etal-2021-discodisco
%X This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. Our system, called DisCoDisCo, is a Transformer-based neural classifier which enhances contextualized word embeddings (CWEs) with hand-crafted features, relying on tokenwise sequence tagging for discourse segmentation and connective detection, and a feature-rich, encoder-less sentence pair classifier for relation classification. Our results for the first two tasks outperform SOTA scores from the previous 2019 shared task, and results on relation classification suggest strong performance on the new 2021 benchmark. Ablation tests show that including features beyond CWEs are helpful for both tasks, and a partial evaluation of multiple pretrained Transformer-based language models indicates that models pre-trained on the Next Sentence Prediction (NSP) task are optimal for relation classification.
%R 10.18653/v1/2021.disrpt-1.6
%U https://aclanthology.org/2021.disrpt-1.6
%U https://doi.org/10.18653/v1/2021.disrpt-1.6
%P 51-62
Markdown (Informal)
[DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection](https://aclanthology.org/2021.disrpt-1.6) (Gessler et al., DISRPT 2021)
ACL
- Luke Gessler, Shabnam Behzad, Yang Janet Liu, Siyao Peng, Yilun Zhu, and Amir Zeldes. 2021. DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection. In Proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021), pages 51–62, Punta Cana, Dominican Republic. Association for Computational Linguistics.