On Refining BERT Contextualized Embeddings using Semantic Lexicons
Résumé
Word vector representations play a fundamental role in many NLP applications. Exploiting human-curated knowledge was proven to improve the quality of word embeddings and their performance on many downstream tasks. Retrofitting is a simple and popular technique for refining distributional word embeddings based on relations coming from a semantic lexicon. Inspired by this technique, we present two methods for incorporating knowledge into contextualized embeddings. We evaluate these methods with BERT embeddings on three biomedical datasets for relation extraction and one movie review dataset for sentiment analysis. We demonstrate that the retrofitted vectors do not substantially impact the performance for these tasks, and conduct a qualitative analysis to provide further insights on this negative result.
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