Character-based Neural Machine Translation Character-based Neural Machine Translation
Paper summary * Most neural machine translation models currently operate on word vectors or one hot vectors of words. * They instead generate the vector of each word on a character-level. * Thereby, the model can spot character-similarities between words and treat them in a similar way. * They do that only for the source language, not for the target language. ### How * They treat each word of the source text on its own. * To each word they then apply the model from [Character-aware neural language models](, i.e. they do per word: * Embed each character into a 620-dimensional space. * Stack these vectors next to each other, resulting in a 2d-tensor in which each column is one of the vectors (i.e. shape `620xN` for `N` characters). * Apply convolutions of size `620xW` to that tensor, where a few different values are used for `W` (i.e. some convolutions cover few characters, some cover many characters). * Apply a tanh after these convolutions. * Apply a max-over-time to the results of the convolutions, i.e. for each convolution use only the maximum value. * Reshape to 1d-vector. * Apply two highway-layers. * They get 1024-dimensional vectors (one per word). * Visualization of their steps: * ![Architecture]( "Architecture") * Afterwards they apply the model from [Neural Machine Translation by Jointly Learning to Align and Translate]( to these vectors, yielding a translation to a target language. * Whenever that translation yields an unknown target-language-word ("UNK"), they replace it with the respective (untranslated) word from the source text. ### Results * They the German-English [WMT]( dataset. * BLEU improvemements (compared to neural translation without character-level words): * German-English improves by about 1.5 points. * English-German improves by about 3 points. * Reduction in the number of unknown target-language-words (same baseline again): * German-English goes down from about 1500 to about 1250. * English-German goes down from about 3150 to about 2650. * Translation examples (Phrase = phrase-based/non-neural translation, NN = non-character-based neural translation, CHAR = theirs): * ![Examples]( "Examples")

Your comment: allows researchers to publish paper summaries that are voted on and ranked!