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    <abstract>Identification of cognates across related languages is one of the primary problems in historical linguistics. Automated cognate identification is helpful for several downstream tasks including identifying sound correspondences, proto-language reconstruction, phylogenetic classification, etc. Previous state-of-the-art methods are mostly based on distributions of phonemes computed across multilingual wordlists and make little use of the cognacy labels that define links among cognate clusters. In this paper, we present a transformer-based architecture inspired by computational biology for the task of automated cognate detection. Beyond a certain amount of supervision, this method performs better than the existing methods, and shows steady improvement with further increase in supervision proving the efficacy of utilizing the labeled information. We also demonstrate that accepting multiple sequence alignments as input and having an end-to-end architecture with link prediction head saves much computation time while simultaneously yielding superior performance.</abstract>
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%0 Conference Proceedings
%T Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer
%A Akavarapu, V.S.D.S.Mahesh
%A Bhattacharya, Arnab
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F akavarapu-bhattacharya-2024-automated
%X Identification of cognates across related languages is one of the primary problems in historical linguistics. Automated cognate identification is helpful for several downstream tasks including identifying sound correspondences, proto-language reconstruction, phylogenetic classification, etc. Previous state-of-the-art methods are mostly based on distributions of phonemes computed across multilingual wordlists and make little use of the cognacy labels that define links among cognate clusters. In this paper, we present a transformer-based architecture inspired by computational biology for the task of automated cognate detection. Beyond a certain amount of supervision, this method performs better than the existing methods, and shows steady improvement with further increase in supervision proving the efficacy of utilizing the labeled information. We also demonstrate that accepting multiple sequence alignments as input and having an end-to-end architecture with link prediction head saves much computation time while simultaneously yielding superior performance.
%U https://aclanthology.org/2024.eacl-long.58/
%P 965-975

Markdown (Informal)

[Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer](https://aclanthology.org/2024.eacl-long.58/) (Akavarapu & Bhattacharya, EACL 2024)

• Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer (Akavarapu & Bhattacharya, EACL 2024)

ACL

• V.S.D.S.Mahesh Akavarapu and Arnab Bhattacharya. 2024. Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 965–975, St. Julian’s, Malta. Association for Computational Linguistics.