WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context
Abstract
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
Keywords
Cite
@article{arxiv.2004.15016,
title = {WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context},
author = {Anna Breit and Artem Revenko and Kiamehr Rezaee and Mohammad Taher Pilehvar and Jose Camacho-Collados},
journal= {arXiv preprint arXiv:2004.15016},
year = {2021}
}
Comments
Accepted to EACL 2021. Reference paper of the SemDeep WiC-TSV challenge: https://competitions.codalab.org/competitions/23683