Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings

• URL: http://arxiv.org/abs/2103.06459v1
• Date: Thu, 11 Mar 2021 04:55:35 GMT
• Title: Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings
• Authors: Linlin Liu, Thien Hai Nguyen, Shafiq Joty, Lidong Bing, Luo Si
• Abstract summary: 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. 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.
• Score: 41.148892848434585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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