Linear Transformations for Cross-lingual Semantic Textual Similarity
Table 1: The mean Pearson correlations over monolingual and cross-lingual tracks of SemEval 2017 STS dataset.
['Monoling.', 'Monoling.', '[BOLD] LC 0.594', '[BOLD] LC IDF 0.703', '[BOLD] PA 0.709', '[BOLD] PA IDF 0.726', '[BOLD] OM 0.713', '[BOLD] OM IDF  [BOLD] 0.756']
i.e., Track4a, Track4b, Track2, and Track6). The size of bilingual dictionaries was set to m=20k. For comparison, we also show the mean Pearson correlation over monolingual tracks (Track5, Track3, and Track1) achieved by the same STS techniques. We can see the clear trend in up-to-down and left-to-right direction. ORT significantly outperformed other transformations independently of STS technique. IDF weighting boosts the correlations in all cases and together with OM approach to STS yields the best performance. An interesting fact is that there is a really significant performance drop when LC is used in cross-lingual environment. PA and OM seem to be much more resistant to weaknesses of linear transformations.