Multilingual Factor Analysis
Table 1: Precision @1 for cross-lingual word similarity tasks. Rows labelled AdvR are copies of Adversarial - Refine rows in conneau2017word. Results marked with a * differ from the ones shown in conneau2017word due to pre-processing done on their part. SVD and IBFA in the semi-supervised setting use the pseudo-dictionary, while AdvR uses frequency information. CSLS is the post-processing technique proposed in conneau2017word.
['Method', 'en-es', 'es-en', 'en-fr', 'fr-en', 'en-de', 'de-en', 'en-ru', 'ru-en', 'en-zh', 'zh-en']
This task is concerned with the problem of retrieving the translation of a given set of source words. We perform an ablation study to assess the effectiveness of our method in the Italian to English (it-en) setting in smith2017offline; dinu2014improving. In these experiments we are interested in studying the effectiveness of our method compared to that of the Procrustes-based fitting used in smith2017offline without any post-processing steps to address the hubness problem (dinu2014improving). We notice that given an expert dictionary, our method performs the best out of all compared methods on all tasks, except in English to Russian (en-ru) translation where it remains competitive. What is surprising is that, in the semi-supervised setting, IBFA bridges the gap between the method proposed in conneau2017word on languages where the dictionary of identical tokens across languages (i.e. the pseudo-dictionary from smith2017offline) is richer. However, even though it significantly outperforms SVD using the pseudo-dictionary, it cannot match the performance of the adversarial approach for more distant languages like English and Chinese (en-zh).