LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content

Shreya Gupta, Parantak Singh, Megha Sundriyal, Md. Shad Akhtar, Tanmoy Chakraborty


Abstract
The conceptualization of a claim lies at the core of argument mining. The segregation of claims is complex, owing to the divergence in textual syntax and context across different distributions. Another pressing issue is the unavailability of labeled unstructured text for experimentation. In this paper, we propose LESA, a framework which aims at advancing headfirst into expunging the former issue by assembling a source-independent generalized model that captures syntactic features through part-of-speech and dependency embeddings, as well as contextual features through a fine-tuned language model. We resolve the latter issue by annotating a Twitter dataset which aims at providing a testing ground on a large unstructured dataset. Experimental results show that LESA improves upon the state-of-the-art performance across six benchmark claim datasets by an average of 3 claim-F1 points for in-domain experiments and by 2 claim-F1 points for general-domain experiments. On our dataset too, LESA outperforms existing baselines by 1 claim-F1 point on the in-domain experiments and 2 claim-F1 points on the general-domain experiments. We also release comprehensive data annotation guidelines compiled during the annotation phase (which was missing in the current literature).
Anthology ID:
2021.eacl-main.277
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3178–3188
Language:
URL:
https://aclanthology.org/2021.eacl-main.277
DOI:
10.18653/v1/2021.eacl-main.277
Bibkey:
Cite (ACL):
Shreya Gupta, Parantak Singh, Megha Sundriyal, Md. Shad Akhtar, and Tanmoy Chakraborty. 2021. LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3178–3188, Online. Association for Computational Linguistics.
Cite (Informal):
LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content (Gupta et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.277.pdf
Code
 LCS2-IIITD/LESA-EACL-2021