Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy
This work addresses the challenge of accurate word meaning representation in NLP for multilingual applications, though it is incremental as it builds on existing models and datasets.
The paper tackled the problem of how well static and contextualized models represent word meanings in context, specifically for homonymy and synonymy, by creating a multilingual dataset and evaluating models across four languages, finding that Transformer-based models effectively disambiguate homonyms but struggle with words of different senses in similar contexts.
This paper presents a multilingual study of word meaning representations in context. We assess the ability of both static and contextualized models to adequately represent different lexical-semantic relations, such as homonymy and synonymy. To do so, we created a new multilingual dataset that allows us to perform a controlled evaluation of several factors such as the impact of the surrounding context or the overlap between words, conveying the same or different senses. A systematic assessment on four scenarios shows that the best monolingual models based on Transformers can adequately disambiguate homonyms in context. However, as they rely heavily on context, these models fail at representing words with different senses when occurring in similar sentences. Experiments are performed in Galician, Portuguese, English, and Spanish, and both the dataset (with more than 3,000 evaluation items) and new models are freely released with this study.