A Wind of Change:Detecting and Evaluating Lexical Semantic Changeacross Times and Domains
Table 4: Mean ρ scores for CD across the alignments. Applies only to RI, SVD and SGNS.
['[BOLD] Dataset', '[BOLD] OP', 'OP−', 'OP+', '[BOLD] WI', '[BOLD] None']
OP+ has the best mean performance on DURel, but performs poorly on SURel. Artetxe et al. show that the additional pre- and post-processing steps of OP+ can be harmful in certain conditions. We tested the influence of the different steps and identified the non-orthogonal whitening transformation as the main reason for a performance drop of ≈20%. As expected, the mean performance drops considerably. However, it remains positive, which suggests that the spaces learned in the models are not random but rather slightly rotated variants. Especially interesting is the comparison of Word Injection (WI) where one common vector space is learned against the OP-models where two separately learned vector spaces are aligned. We found that OP profits from mean-centering in the pre-processing step: applying mean-centering to WI matrices improves the performance by 3% on WI+SGNS+CD.