Learning Joint Multilingual Sentence Representations with Neural Machine Translation Published at the 2nd ACL Workshop on Representation Learning for NLP, Vancouver, August 3rd 2017. This version contains slightly updated results and examples.
Table 4: Pair-wise error rates of similarity search for 6 languages (UN Dev). Training was performed with a one layer BLSTM with 512 hiddens, max-pooling and the “efsraz-all” strategy.
['Src', 'Target language En', 'Target language Fr', 'Target language Es', 'Target language Ru', 'Target language Ar', 'Target language Zh', 'Target language All']
Overall, the error rates vary only slightly from the average of 1.2% although the six languages differ significantly with respect to morphology, inflection, word order, etc. In particular, Chinese is handled as well as the other languages. This is in nice contrast to many other NLP application, in particular NMT, for which the performances on Chinese are significantly below those of other languages. All error rates are below 1.7%.