WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context
This provides a flexible benchmark for evaluating models in Word Sense Disambiguation across domains, but it is incremental as it builds on existing datasets and tasks.
The authors tackled the problem of Word Sense Disambiguation by introducing WiC-TSV, a multi-domain evaluation benchmark for Target Sense Verification, and found that state-of-the-art language models perform decently but still lag behind human performance, especially in out-of-domain settings.
We present WiC-TSV, a new multi-domain evaluation benchmark for Word Sense Disambiguation. More specifically, we introduce a framework for Target Sense Verification of Words in Context which grounds its uniqueness in the formulation as a binary classification task thus being independent of external sense inventories, and the coverage of various domains. This makes the dataset highly flexible for the evaluation of a diverse set of models and systems in and across domains. WiC-TSV provides three different evaluation settings, depending on the input signals provided to the model. We set baseline performance on the dataset using state-of-the-art language models. Experimental results show that even though these models can perform decently on the task, there remains a gap between machine and human performance, especially in out-of-domain settings. WiC-TSV data is available at https://competitions.codalab.org/competitions/23683