drsphelps at SemEval-2022 Task 2: Learning idiom representations using BERTRAM
About
This paper describes our system for SemEval-2022 Task 2 Multilingual Idiomaticity Detection and Sentence Embedding sub-task B. We modify a standard BERT sentence transformer by adding embeddings for each idioms, which are created using BERTRAM and a small number of contexts. We show that this technique increases the quality of idiom representations and leads to better performance on the task. We also perform analysis on our final results and show that the quality of the produced idiom embeddings is highly sensitive to the quality of the input contexts.
Dylan Phelps• 2022
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Semantic Textual Similarity | English STS | Average Score76.43 | 68 | |
| Semantic Textual Similarity | SemEval-2022 Task 2 Idiomatic STS (evaluation) | Spearman Rho (All)0.6504 | 14 | |
| Semantic Textual Similarity | SemEval STS Portuguese (PT) | Overall Score73.07 | 3 | |
| Semantic Textual Similarity | SemEval STS Galician (GL) | MWE Score29.24 | 3 |
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