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    <abstract>Parallel data is difficult to obtain for low-resource languages in machine translation tasks, making it crucial to leverage monolingual linguistic features as auxiliary information. This article introduces a novel integration of hypernym features into the model by combining learnable hypernym embeddings with word embeddings, providing semantic information. Experimental results based on bilingual and multilingual models showed that: (1) incorporating hypernyms improves translation quality in low-resource settings, yielding +1.7 BLEU scores for bilingual models, (2) the hypernym feature demonstrates efficacy both in isolation and in conjunction with syntactic features, and (3) the performance is influenced by the choice of feature combination operators and hypernym-path hyperparameters.</abstract>
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%0 Conference Proceedings
%T Incorporating Hypernym Features for Improving Low-resource Neural Machine Translation
%A Chakrabarty, Abhisek
%A Song, Haiyue
%A Dabre, Raj
%A Tanaka, Hideki
%A Utiyama, Masao
%Y Tezcan, Arda
%Y Sánchez-Cartagena, Víctor M.
%Y Esplà-Gomis, Miquel
%S Proceedings of the First International Workshop on Knowledge-Enhanced Machine Translation
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, United Kingdom
%F chakrabarty-etal-2024-incorporating
%X Parallel data is difficult to obtain for low-resource languages in machine translation tasks, making it crucial to leverage monolingual linguistic features as auxiliary information. This article introduces a novel integration of hypernym features into the model by combining learnable hypernym embeddings with word embeddings, providing semantic information. Experimental results based on bilingual and multilingual models showed that: (1) incorporating hypernyms improves translation quality in low-resource settings, yielding +1.7 BLEU scores for bilingual models, (2) the hypernym feature demonstrates efficacy both in isolation and in conjunction with syntactic features, and (3) the performance is influenced by the choice of feature combination operators and hypernym-path hyperparameters.
%U https://aclanthology.org/2024.kemt-1.1
%P 1-6

Markdown (Informal)

[Incorporating Hypernym Features for Improving Low-resource Neural Machine Translation](https://aclanthology.org/2024.kemt-1.1) (Chakrabarty et al., KEMT-WS 2024)

• Incorporating Hypernym Features for Improving Low-resource Neural Machine Translation (Chakrabarty et al., KEMT-WS 2024)

ACL

• Abhisek Chakrabarty, Haiyue Song, Raj Dabre, Hideki Tanaka, and Masao Utiyama. 2024. Incorporating Hypernym Features for Improving Low-resource Neural Machine Translation. In Proceedings of the First International Workshop on Knowledge-Enhanced Machine Translation, pages 1–6, Sheffield, United Kingdom. European Association for Machine Translation (EAMT).