Word-level Lexical Normalisation using Context-Dependent Embeddings
Table 8: The errors made by the ELMo and Cauchy models on the US Accidents dataset.
['[EMPTY]', 'ELMo', 'Cauchy']
An analysis of the errors produced by the ELMo-initialised model provides clarity as to why it is outperformed by the Cauchy embeddings for the US Accidents dataset. 29 of the 80 errors made by the ELMo-based model were measurements, such as “10’h” being normalised to the incorrect measurement of “8’ high”. The Cauchy embeddings did not make any errors on such terms.