Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind

TL;DR

This paper addresses the challenge of aligning cross-lingual contextual word embeddings for multi-sense words by proposing methods that improve alignment accuracy without degrading overall performance, especially in unsupervised settings.

Contribution

It introduces two novel solutions for better handling multi-sense words in cross-lingual embedding alignment, enhancing unsupervised bilingual lexicon induction.

Findings

Improved supervised alignment for multi-sense words without harming macro performance.

Significant over 10-point improvement in unsupervised bilingual lexicon induction.

Effective handling of multi-sense words in cross-lingual embedding tasks.

Abstract

Recent work in cross-lingual contextual word embedding learning cannot handle multi-sense words well. In this work, we explore the characteristics of contextual word embeddings and show the link between contextual word embeddings and word senses. We propose two improving solutions by considering contextual multi-sense word embeddings as noise (removal) and by generating cluster level average anchor embeddings for contextual multi-sense word embeddings (replacement). Experiments show that our solutions can improve the supervised contextual word embeddings alignment for multi-sense words in a microscopic perspective without hurting the macroscopic performance on the bilingual lexicon induction task. For unsupervised alignment, our methods significantly improve the performance on the bilingual lexicon induction task for more than 10 points.