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    <abstract>Cross-lingual word embeddings (CLWE) have been proven useful in many cross-lingual tasks. However, most existing approaches to learn CLWE including the ones with contextual embeddings are sense agnostic. In this work, we propose a novel framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only. We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly. The monolingual ELMo and BERT models pretrained with our sense-aware cross entropy loss demonstrate significant performance improvement for word sense disambiguation tasks. We then propose a sense alignment objective on top of the sense-aware cross entropy loss for cross-lingual model pretraining, and pretrain cross-lingual models for several language pairs (English to German/Spanish/Japanese/Chinese). Compared with the best baseline results, our cross-lingual models achieve 0.52%, 2.09% and 1.29% average performance improvements on zero-shot cross-lingual NER, sentiment classification and XNLI tasks, respectively.</abstract>
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%0 Conference Proceedings
%T Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings
%A Liu, Linlin
%A Nguyen, Thien Hai
%A Joty, Shafiq
%A Bing, Lidong
%A Si, Luo
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F liu-etal-2022-towards-multi
%X Cross-lingual word embeddings (CLWE) have been proven useful in many cross-lingual tasks. However, most existing approaches to learn CLWE including the ones with contextual embeddings are sense agnostic. In this work, we propose a novel framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only. We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly. The monolingual ELMo and BERT models pretrained with our sense-aware cross entropy loss demonstrate significant performance improvement for word sense disambiguation tasks. We then propose a sense alignment objective on top of the sense-aware cross entropy loss for cross-lingual model pretraining, and pretrain cross-lingual models for several language pairs (English to German/Spanish/Japanese/Chinese). Compared with the best baseline results, our cross-lingual models achieve 0.52%, 2.09% and 1.29% average performance improvements on zero-shot cross-lingual NER, sentiment classification and XNLI tasks, respectively.
%U https://aclanthology.org/2022.coling-1.386
%P 4381-4396

Markdown (Informal)

[Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings](https://aclanthology.org/2022.coling-1.386) (Liu et al., COLING 2022)

• Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings (Liu et al., COLING 2022)

ACL

• Linlin Liu, Thien Hai Nguyen, Shafiq Joty, Lidong Bing, and Luo Si. 2022. Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4381–4396, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.