To lemmatize or not to lemmatize: how word normalisation affects ELMo performance in word sense disambiguation
Table 4: Averaged macro-F1 scores for WSD
['[BOLD] Model', '[BOLD] English', '[BOLD] Russian']
ELMo models can be employed for the WSD task in two different ways: either by fine-tuning the model or by extracting word representations from it and then using them as features in a downstream classifier. We decided to stick to the second (feature extraction) approach, since it is conceptually and computationally simpler. Additionally, peters-etal-2019-tune showed that for most NLP tasks (except those focused on sentence pairs) the performance of feature extraction and fine-tuning is nearly the same.