Moving Down the Long Tail of Word Sense Disambiguationwith Gloss Informed Bi-encoders
Table 1: F1-score (%) on the English all-words WSD task. ALL is the concatenation of all datasets, including the development set SE07. We compare our bi-encoder model (BEM) against the WordNet S1 and most frequent sense (MFS) baselines, as well as a frozen BERT-base classifier and recent prior work on this task.
['[EMPTY]', '[BOLD] Dev SE07', '[BOLD] Test Datasets SE2', '[BOLD] Test Datasets SE3', '[BOLD] Test Datasets SE13', '[BOLD] Test Datasets SE15', '[BOLD] Concatenation of all Datasets Nouns', '[BOLD] Concatenation of all Datasets Verbs', '[BOLD] Concatenation of all Datasets Adj.', '[BOLD] Concatenation of all Datasets Adv.', '[BOLD] Concatenation of all Datasets ALL']
all-words WSD task Raganato et al. Frozen BERT-base is a strong baseline, outperforming all of the prior work that does not incorporate pretraining into their systems (GASext, HCAN, and EWISE). The GLU and SVC systems, which use the representations learned by BERT without fine-tuning, both slightly outperform our pretrained baseline. GlossBERT achieves even better WSD performance by fine-tuning BERT with their cross-encoder approach.