Proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021)

Amir Zeldes, Yang Janet Liu, Mikel Iruskieta, Philippe Muller, Chloé Braud, Sonia Badene (Editors)


Anthology ID:
2021.disrpt-1
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
DISRPT | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/2021.disrpt-1
DOI:
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Proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021)
Amir Zeldes | Yang Janet Liu | Mikel Iruskieta | Philippe Muller | Chloé Braud | Sonia Badene

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The DISRPT 2021 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation ClassificationDISRPT 2021 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification
Amir Zeldes | Yang Janet Liu | Mikel Iruskieta | Philippe Muller | Chloé Braud | Sonia Badene

In 2021, we organized the second iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms : the DISRPT Shared Task (Discourse Relation Parsing and Treebanking). Adding to the 2019 tasks on Elementary Discourse Unit Segmentation and Connective Detection, this iteration of the Shared Task included for the first time a track on discourse relation classification across three formalisms : RST, SDRT, and PDTB. In this paper we review the data included in the Shared Task, which covers nearly 3 million manually annotated tokens from 16 datasets in 11 languages, survey and compare submitted systems and report on system performance on each task for both annotated and plain-tokenized versions of the data.

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Multi-lingual Discourse Segmentation and Connective Identification : MELODI at Disrpt2021MELODI at Disrpt2021
Morteza Kamaladdini Ezzabady | Philippe Muller | Chloé Braud

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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Delexicalised Multilingual Discourse Segmentation for DISRPT 2021 and Tense, Mood, Voice and Modality Tagging for 11 LanguagesDISRPT 2021 and Tense, Mood, Voice and Modality Tagging for 11 Languages
Tillmann Dönicke

This paper describes our participating system for the Shared Task on Discourse Segmentation and Connective Identification across Formalisms and Languages. Key features of the presented approach are the formulation as a clause-level classification task, a language-independent feature inventory based on Universal Dependencies grammar, and composite-verb-form analysis. The achieved F1 is 92 % for German and English and lower for other languages. The paper also presents a clause-level tagger for grammatical tense, aspect, mood, voice and modality in 11 languages.