Lessons from Natural Language Inference in the Clinical Domain
Table 6: Absolute gain in accuracy with respect to the baseline (GloVe[CC]) for different word embeddings (the number in parentheses reflects the number of tokens in the corresponding training corpora).
['[BOLD] Embeddings', '[BOLD] BOW', '[BOLD] InferSent', '[BOLD] ESIM']
Furthermore, the InferSent models achieves a 3.1% boost with the fastText Wikipedia embeddings, fine-tuned on the MIMIC-III data. Note that the results fastText[Wiki] are worse than the baseline GloVe[CC] for all models, which could be due to the source corpus size. However, the results on BioASQ are worse than on MIMIC-III, despite the significantly larger corpus of the BioASQ embeddings. Overall, our experiments show the benefit of domain-specific rather than general-domain word embeddings.