A Bilingual Generative Transformer for Semantic Sentence Embedding
Table 4: Performance measured in Pearson’s r×100, on the SemEval 2017 STS task on the es-es, ar-ar, en-es, en-ar, and en-tr datasets.
['Model', 'es-es', 'ar-ar', 'en-es', 'en-ar', 'en-tr']
From these results, we see that BGT has the best performance across all datasets, however the performance is significantly stronger than the BilingualTrans and BGT w/o Prior baselines in the cross-lingual setting. Since BGT w/o LangVars also has significantly better performance on these tasks, most of this gain seems to be due to the prior have a regularizing effect. However, BGT outperforms BGT w/o LangVars overall, and we hypothesize that the gap in performance between these two models is due to BGT being able to strip away the language-specific information in the representations with its language-specific variables, allowing for the semantics of the sentences to be more directly compared.