Lost in Context? On the Sense-Wise Variance of Contextualized Word Embeddings

Contextualized word embeddings in language models have given much advance to NLP. Intuitively, sentential information is integrated into the representation of words, which can help model polysemy. However, context sensitivity also leads to the variance of representations, which may break the semantic consistency for synonyms. Previous works that investigate contextualized sensitivity focus on the
token
level representations, while we are taking a deeper dive into exploring representations at the fine-grained
sense
level. In particular, we quantify how much the contextualized embeddings of each word sense vary across contexts in typical pre-trained models, the results show that contextualized embeddings can be highly consistent across contexts, even for two different words with the same sense. In addition, part-of-speech, number of word senses, and sentence length have an influence on the variance of sense representations. Interestingly, we find that word representations are position-biased, where the first words in different contexts tend to be more similar. We analyze such a phenomenon and also propose a prompt-augmentation method to alleviate such bias in distance-based word sense disambiguation settings. Finally, we investigate the influence of sense-level pre-training on the performance of different downstream tasks, results show that such external tasks can improve the sense- and syntactic-related tasks, while not necessarily benefiting general language understanding tasks.