Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
Table 3: : F1-scores on the TED corpus document classification task when training and evaluating on the same language. Results other than Bridge CorrNet are taken from [Hermann and Blunsom2014b].
['Setting', 'Languages Arabic', 'Languages German', 'Languages Spanish', 'Languages French', 'Languages Italian', 'Languages Dutch', 'Languages Polish', 'Languages Pt-Br', 'Languages Rom’n', 'Languages Russian', 'Languages Turkish']
The idea here is to see if learning common representations for multiple views can also help in improving the performance of a task involving only one view. \newciteHermannK2014 argue that a Naive Bayes (NB) classifier trained using a bag-of-words representation of the documents is a very strong baseline. In fact, a classifier trained on document representations learned using their model does not beat a NB classifier for the task of monolingual classification. On the other hand our model is able to beat NB for 5/11 languages. Further, for 4 other languages (German, French, Romanian, Russian) its performance is only marginally poor than that of NB.