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        <title>Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages</title>
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        <namePart type="given">Pranaydeep</namePart>
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    <abstract>This paper reports on experiments for cross-lingual transfer using the anchor-based approach of Schuster et al. (2019) for English and a low-resourced language, namely Hindi. For the sake of comparison, we also evaluate the approach on three very different higher-resourced languages, viz. Dutch, Russian and Chinese. Initially designed for ELMo embeddings, we analyze the approach for the more recent BERT family of transformers for a variety of tasks, both mono and cross-lingual. The results largely prove that like most other cross-lingual transfer approaches, the static anchor approach is underwhelming for the low-resource language, while performing adequately for the higher resourced ones. We attempt to provide insights into both the quality of the anchors, and the performance for low-shot cross-lingual transfer to better understand this performance gap. We make the extracted anchors and the modified train and test sets available for future research at https://github.com/pranaydeeps/Vyaapak</abstract>
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%0 Conference Proceedings
%T Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages
%A Singh, Pranaydeep
%A De Clercq, Orphee
%A Lefever, Els
%Y Melero, Maite
%Y Sakti, Sakriani
%Y Soria, Claudia
%S Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F singh-etal-2022-investigating
%X This paper reports on experiments for cross-lingual transfer using the anchor-based approach of Schuster et al. (2019) for English and a low-resourced language, namely Hindi. For the sake of comparison, we also evaluate the approach on three very different higher-resourced languages, viz. Dutch, Russian and Chinese. Initially designed for ELMo embeddings, we analyze the approach for the more recent BERT family of transformers for a variety of tasks, both mono and cross-lingual. The results largely prove that like most other cross-lingual transfer approaches, the static anchor approach is underwhelming for the low-resource language, while performing adequately for the higher resourced ones. We attempt to provide insights into both the quality of the anchors, and the performance for low-shot cross-lingual transfer to better understand this performance gap. We make the extracted anchors and the modified train and test sets available for future research at https://github.com/pranaydeeps/Vyaapak
%U https://aclanthology.org/2022.sigul-1.23
%P 176-184

Markdown (Informal)

[Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages](https://aclanthology.org/2022.sigul-1.23) (Singh et al., SIGUL 2022)

• Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages (Singh et al., SIGUL 2022)

ACL

• Pranaydeep Singh, Orphee De Clercq, and Els Lefever. 2022. Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages. In Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages, pages 176–184, Marseille, France. European Language Resources Association.