@inproceedings{chaixanien-etal-2024-pungene,
title = "Pungene at {D}ial{AM}-2024: Identification of Propositional and Illocutionary Relations",
author = "Chaixanien, Sirawut and
Choi, Eugene and
Shaar, Shaden and
Cardie, Claire",
editor = "Ajjour, Yamen and
Bar-Haim, Roy and
El Baff, Roxanne and
Liu, Zhexiong and
Skitalinskaya, Gabriella",
booktitle = "Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.argmining-1.12",
pages = "119--123",
abstract = "In this paper we tackle the shared task DialAM-2024 aiming to annotate dialogue based on the inference anchoring theory (IAT). The task can be split into two parts, identification of propositional relations and identification of illocutionary relations. We propose a pipelined system made up of three parts: (1) locutionary-propositions relation detection, (2) propositional relations detection, and (3) illocutionary relations identification. We fine-tune models independently for each step, and combine at the end for the final system. Our proposed system ranks second overall compared to other participants in the shared task, scoring an average f1-score on both sub-parts of 63.7.",
}
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%0 Conference Proceedings
%T Pungene at DialAM-2024: Identification of Propositional and Illocutionary Relations
%A Chaixanien, Sirawut
%A Choi, Eugene
%A Shaar, Shaden
%A Cardie, Claire
%Y Ajjour, Yamen
%Y Bar-Haim, Roy
%Y El Baff, Roxanne
%Y Liu, Zhexiong
%Y Skitalinskaya, Gabriella
%S Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F chaixanien-etal-2024-pungene
%X In this paper we tackle the shared task DialAM-2024 aiming to annotate dialogue based on the inference anchoring theory (IAT). The task can be split into two parts, identification of propositional relations and identification of illocutionary relations. We propose a pipelined system made up of three parts: (1) locutionary-propositions relation detection, (2) propositional relations detection, and (3) illocutionary relations identification. We fine-tune models independently for each step, and combine at the end for the final system. Our proposed system ranks second overall compared to other participants in the shared task, scoring an average f1-score on both sub-parts of 63.7.
%U https://aclanthology.org/2024.argmining-1.12
%P 119-123
Markdown (Informal)
[Pungene at DialAM-2024: Identification of Propositional and Illocutionary Relations](https://aclanthology.org/2024.argmining-1.12) (Chaixanien et al., ArgMining 2024)
ACL