UAlberta at SemEval 2022 Task 2: Leveraging Glosses and Translations for Multilingual Idiomaticity Detection
This work addresses idiomaticity detection for natural language processing applications, but it is incremental as it builds on existing linguistic assumptions and knowledge sources.
The paper tackled multilingual idiomaticity detection by developing two methods: one integrating word meanings into a classifier based on noncompositionality, and another using translation differences with a lexical knowledge base. The results supported both approaches, with the first performing particularly well, though no concrete numbers were provided in the abstract.
We describe the University of Alberta systems for the SemEval-2022 Task 2 on multilingual idiomaticity detection. Working under the assumption that idiomatic expressions are noncompositional, our first method integrates information on the meanings of the individual words of an expression into a binary classifier. Further hypothesizing that literal and idiomatic expressions translate differently, our second method translates an expression in context, and uses a lexical knowledge base to determine if the translation is literal. Our approaches are grounded in linguistic phenomena, and leverage existing sources of lexical knowledge. Our results offer support for both approaches, particularly the former.