A Comparative Study of Lexical Substitution Approachesbased on Neural Language Models
Table 3: Evaluation on the word sense induction task to the current state-of-the-art models on SemEval2010 and SemEval2013 tasks.
['[BOLD] Model', '[BOLD] SemEv10', '[BOLD] SemEv13']
We evaluate lexical substitutes based on neural LMs in the following datasets: SemEval-2013 and SemEval-2010. For example, a combination of BERT with its embeddings (BERT+embs) improves the results of a BERT model by about 3% on both data sets. Likewise, a combination of forward LM, backward LM and proximity of ELMo embeddings between substitute and target word, i.e. ELMo+embs generator, raises results on SemEval-2010 task by about 4%.