LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)


This paper describes the LIAAD system that was ranked second place in the Wordin-Context challenge (WiC) featured in SemDeep-5. Our solution is based on a novel system for Word Sense Disambiguation (WSD) using contextual embeddings and full-inventory sense embeddings. We adapt this WSD system, in a straightforward manner, for the present task of detecting whether the same sense occurs in a pair of sentences. Additionally, we show that our solution is able to achieve competitive performance even without using the provided training or development sets, mitigating potential concerns related to task overfitting.

In Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)


Link to the WiC task website.

Daniel Loureiro
PhD candidate in Computer Science

Interested in more robust and interpretable NLP. Curious about Neural/Logic hybrids.