Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation
Table 5: Effect of machine translation data over our proposed Encoder-Classifier on the SNLI tasks. The results of SNLI (Fr) shows the zero-shot performance of our system.
['[BOLD] Parallel data type for NMT', 'SNLI (En)', 'SNLI (Fr)']
We explore two dimensions that could affect zero-shot performance related to our training data in the multilingual NMT model. First, we investigate the effect of using symmetric training data to train both directions in the multilingual NMT system. We conduct an experiment where we take half of the sentences from the En→Fr training set and use the swapped version of the other half of the sentences for training the model. Second, we want to see the effect of training data size, so we run an experiment where we use only half of the training set in a symmetric fashion. However, both the symmetric and asymmetric versions of the data perform comparably on both tasks. This shows that the multilingual NMT system is able to learn an effective interlingua even without the need of symmetric data across the language pairs involved.