@inproceedings{rezaee-etal-2021-cross,
title = "On the Cross-lingual Transferability of Contextualized Sense Embeddings",
author = "Rezaee, Kiamehr and
Loureiro, Daniel and
Camacho-Collados, Jose and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrl-1.10",
doi = "10.18653/v1/2021.mrl-1.10",
pages = "107--115",
abstract = "In this paper we analyze the extent to which contextualized sense embeddings, i.e., sense embeddings that are computed based on contextualized word embeddings, are transferable across languages.To this end, we compiled a unified cross-lingual benchmark for Word Sense Disambiguation. We then propose two simple strategies to transfer sense-specific knowledge across languages and test them on the benchmark.Experimental results show that this contextualized knowledge can be effectively transferred to similar languages through pre-trained multilingual language models, to the extent that they can out-perform monolingual representations learnednfrom existing language-specific data.",
}
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%0 Conference Proceedings
%T On the Cross-lingual Transferability of Contextualized Sense Embeddings
%A Rezaee, Kiamehr
%A Loureiro, Daniel
%A Camacho-Collados, Jose
%A Pilehvar, Mohammad Taher
%S Proceedings of the 1st Workshop on Multilingual Representation Learning
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F rezaee-etal-2021-cross
%X In this paper we analyze the extent to which contextualized sense embeddings, i.e., sense embeddings that are computed based on contextualized word embeddings, are transferable across languages.To this end, we compiled a unified cross-lingual benchmark for Word Sense Disambiguation. We then propose two simple strategies to transfer sense-specific knowledge across languages and test them on the benchmark.Experimental results show that this contextualized knowledge can be effectively transferred to similar languages through pre-trained multilingual language models, to the extent that they can out-perform monolingual representations learnednfrom existing language-specific data.
%R 10.18653/v1/2021.mrl-1.10
%U https://aclanthology.org/2021.mrl-1.10
%U https://doi.org/10.18653/v1/2021.mrl-1.10
%P 107-115
Markdown (Informal)
[On the Cross-lingual Transferability of Contextualized Sense Embeddings](https://aclanthology.org/2021.mrl-1.10) (Rezaee et al., MRL 2021)
ACL