@inproceedings{krishnan-ragavan-2021-morphology,
title = "Morphology-Aware Meta-Embeddings for {T}amil",
author = "Krishnan, Arjun Sai and
Ragavan, Seyoon",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-srw.13",
doi = "10.18653/v1/2021.naacl-srw.13",
pages = "94--111",
abstract = "In this work, we explore generating morphologically enhanced word embeddings for Tamil, a highly agglutinative South Indian language with rich morphology that remains low-resource with regards to NLP tasks. We present here the first-ever word analogy dataset for Tamil, consisting of 4499 hand-curated word tetrads across 10 semantic and 13 morphological relation types. Using a rules-based segmenter to capture morphology as well as meta-embedding techniques, we train meta-embeddings that outperform existing baselines by 16{\%} on our analogy task and appear to mitigate a previously observed trade-off between semantic and morphological accuracy.",
}
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%0 Conference Proceedings
%T Morphology-Aware Meta-Embeddings for Tamil
%A Krishnan, Arjun Sai
%A Ragavan, Seyoon
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Online
%F krishnan-ragavan-2021-morphology
%X In this work, we explore generating morphologically enhanced word embeddings for Tamil, a highly agglutinative South Indian language with rich morphology that remains low-resource with regards to NLP tasks. We present here the first-ever word analogy dataset for Tamil, consisting of 4499 hand-curated word tetrads across 10 semantic and 13 morphological relation types. Using a rules-based segmenter to capture morphology as well as meta-embedding techniques, we train meta-embeddings that outperform existing baselines by 16% on our analogy task and appear to mitigate a previously observed trade-off between semantic and morphological accuracy.
%R 10.18653/v1/2021.naacl-srw.13
%U https://aclanthology.org/2021.naacl-srw.13
%U https://doi.org/10.18653/v1/2021.naacl-srw.13
%P 94-111
Markdown (Informal)
[Morphology-Aware Meta-Embeddings for Tamil](https://aclanthology.org/2021.naacl-srw.13) (Krishnan & Ragavan, NAACL 2021)
ACL
- Arjun Sai Krishnan and Seyoon Ragavan. 2021. Morphology-Aware Meta-Embeddings for Tamil. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 94–111, Online. Association for Computational Linguistics.