On the Linguistic Representational Power of Neural Machine Translation Models
Table 15: Impact of changing the target language on POS tagging accuracy with classifiers trained on the encoder side. Self = German/Czech in rows 1/2 respectively.
['Source language', 'Target language English', 'Target language Arabic', 'Target language Self']
Our results showed that the representations that are learned when translating into English are better for predicting POS or morphology than those learned when translating into German, which are in turn better than those learned when translating into Hebrew. The inherent difficulty in translating Arabic to (morphologically rich) Hebrew/German languages may affect the ability to learn good representations of word structure, or perhaps more data is required in the case of these languages to learn Arabic representations of the same quality. We found these results to be consistent in other language pairs, i.e., by changing the source from Arabic to German and Czech and when training character-based models instead of word-based models.