To lemmatize or not to lemmatize: how word normalisation affects ELMo performance in word sense disambiguation
This addresses text pre-processing decisions for NLP practitioners working with morphologically rich languages, though it is incremental as it builds on existing ELMo models.
The study tested whether lemmatization improves ELMo performance in word sense disambiguation, finding it unnecessary for English but providing small, consistent gains for Russian, a rich-morphology language.
We critically evaluate the widespread assumption that deep learning NLP models do not require lemmatized input. To test this, we trained versions of contextualised word embedding ELMo models on raw tokenized corpora and on the corpora with word tokens replaced by their lemmas. Then, these models were evaluated on the word sense disambiguation task. This was done for the English and Russian languages. The experiments showed that while lemmatization is indeed not necessary for English, the situation is different for Russian. It seems that for rich-morphology languages, using lemmatized training and testing data yields small but consistent improvements: at least for word sense disambiguation. This means that the decisions about text pre-processing before training ELMo should consider the linguistic nature of the language in question.