A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit

Jivnesh Sandhan, Ashish Gupta, Hrishikesh Terdalkar, Tushar Sandhan, Suvendu Samanta, Laxmidhar Behera, Pawan Goyal


Abstract
The phenomenon of compounding is ubiquitous in Sanskrit. It serves for achieving brevity in expressing thoughts, while simultaneously enriching the lexical and structural formation of the language. In this work, we focus on the Sanskrit Compound Type Identification (SaCTI) task, where we consider the problem of identifying semantic relations between the components of a compound word. Earlier approaches solely rely on the lexical information obtained from the components and ignore the most crucial contextual and syntactic information useful for SaCTI. However, the SaCTI task is challenging primarily due to the implicitly encoded context-sensitive semantic relation between the compound components. Thus, we propose a novel multi-task learning architecture which incorporates the contextual information and enriches the complementary syntactic information using morphological tagging and dependency parsing as two auxiliary tasks. Experiments on the benchmark datasets for SaCTI show 6.1 points (Accuracy) and 7.7 points (F1-score) absolute gain compared to the state-of-the-art system. Further, our multi-lingual experiments demonstrate the efficacy of the proposed architecture in English and Marathi languages.
Anthology ID:
2022.coling-1.358
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4071–4083
Language:
URL:
https://aclanthology.org/2022.coling-1.358
DOI:
Bibkey:
Cite (ACL):
Jivnesh Sandhan, Ashish Gupta, Hrishikesh Terdalkar, Tushar Sandhan, Suvendu Samanta, Laxmidhar Behera, and Pawan Goyal. 2022. A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4071–4083, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit (Sandhan et al., COLING 2022)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2022.coling-1.358.pdf
Code
 ashishgupta2598/sacti