@inproceedings{sazid-mercer-2022-unified,
title = "A Unified Representation and a Decoupled Deep Learning Architecture for Argumentation Mining of Students{'} Persuasive Essays",
author = "Sazid, Muhammad Tawsif and
Mercer, Robert E.",
editor = "Lapesa, Gabriella and
Schneider, Jodi and
Jo, Yohan and
Saha, Sougata",
booktitle = "Proceedings of the 9th Workshop on Argument Mining",
month = oct,
year = "2022",
address = "Online and in Gyeongju, Republic of Korea",
publisher = "International Conference on Computational Linguistics",
url = "https://aclanthology.org/2022.argmining-1.6",
pages = "74--83",
abstract = "We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components{---}premises, claims, and major claims{---}and the argumentative relations{---}premise to claim or premise in a support or attack relation, and claim to major-claim in a for or against relation{---}in an end-to-end machine learning pipeline. This tightly integrated representation combines the component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtain state-of-the-art accuracy on the Persuasive Essays dataset. Also, we have introduced a decoupled solution to identify the entities and relations first, and on top of that, a second model is used to detect distance between the detected related components. An augmentation of the corpus (paragraph version) by including copies of major claims has further increased the performance.",
}
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<abstract>We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components—premises, claims, and major claims—and the argumentative relations—premise to claim or premise in a support or attack relation, and claim to major-claim in a for or against relation—in an end-to-end machine learning pipeline. This tightly integrated representation combines the component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtain state-of-the-art accuracy on the Persuasive Essays dataset. Also, we have introduced a decoupled solution to identify the entities and relations first, and on top of that, a second model is used to detect distance between the detected related components. An augmentation of the corpus (paragraph version) by including copies of major claims has further increased the performance.</abstract>
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%0 Conference Proceedings
%T A Unified Representation and a Decoupled Deep Learning Architecture for Argumentation Mining of Students’ Persuasive Essays
%A Sazid, Muhammad Tawsif
%A Mercer, Robert E.
%Y Lapesa, Gabriella
%Y Schneider, Jodi
%Y Jo, Yohan
%Y Saha, Sougata
%S Proceedings of the 9th Workshop on Argument Mining
%D 2022
%8 October
%I International Conference on Computational Linguistics
%C Online and in Gyeongju, Republic of Korea
%F sazid-mercer-2022-unified
%X We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components—premises, claims, and major claims—and the argumentative relations—premise to claim or premise in a support or attack relation, and claim to major-claim in a for or against relation—in an end-to-end machine learning pipeline. This tightly integrated representation combines the component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtain state-of-the-art accuracy on the Persuasive Essays dataset. Also, we have introduced a decoupled solution to identify the entities and relations first, and on top of that, a second model is used to detect distance between the detected related components. An augmentation of the corpus (paragraph version) by including copies of major claims has further increased the performance.
%U https://aclanthology.org/2022.argmining-1.6
%P 74-83
Markdown (Informal)
[A Unified Representation and a Decoupled Deep Learning Architecture for Argumentation Mining of Students’ Persuasive Essays](https://aclanthology.org/2022.argmining-1.6) (Sazid & Mercer, ArgMining 2022)
ACL