What We’re Reading: Single-Queue Decoding for Neural Machine Translation

The most popular way of finding a translation for a source sentence with a neural sequence-to-sequence model is a simple beam search. The target sentence is predicted one word at a time and after each prediction, a fixed number of possibilities (typically between 4 and 10) is retained for further exploration. This strategy can be… Read More What We’re Reading: Single-Queue Decoding for Neural Machine Translation

2017 Machine Translation Quality Evaluation Addendum

This post is an addendum to our original post on 1/10/2017 entitled 2017 Machine Translation Quality Evaluation. Experimental Design We evaluate all machine translation systems for English-French and English-German. We report case-insensitive BLEU-4 [2], which is computed by the mteval scoring script from the Stanford University open source toolkit Phrasal (https://github.com/stanfordnlp/phrasal). NIST tokenization was applied… Read More 2017 Machine Translation Quality Evaluation Addendum