Neural Machine Translation With Noisy Lexical Constraints

Improving Lexical Choice in Neural Machine Translation

We explore two solutions to the problem of mistranslating rare words in neural machine translation. First, we argue that the standard output layer, which computes the inner product of a vector representing the context with all possible output word embeddings, rewards frequent words disproportionately, and we propose to fix the norms of both vectors to a constant value. Second, we integrate a si...

Lexical selection for machine translation

Lexical Functions And Machine Translation

This paper discusses the lexicographical concept of lexical functions (Mel'~uk and Zolkovsky, 1984) and their potential exploitation in the development of a machine translation lexicon designed to handle collocations. We show how lexical functions can be thought to reflect cross-linguistic meaning concepts for collocational structures and their translational equivalents, and therefore suggest t...

Neural Machine Translation with Reconstruction

Although end-to-end Neural Machine Translation (NMT) has achieved remarkable progress in the past two years, it suffers from a major drawback: translations generated by NMT systems often lack of adequacy. It has been widely observed that NMT tends to repeatedly translate some source words while mistakenly ignoring other words. To alleviate this problem, we propose a novel encoder-decoder-recons...

Controlling Target Features in Neural Machine Translation via Prefix Constraints

We propose prefix constraints, a novel method to enforce constraints on target sentences in neural machine translation. It places a sequence of special tokens at the beginning of target sentence (target prefix), while side constraints (Sennrich et al., 2016) places a special token at the end of source sentence (source suffix). Prefix constraints can be predicted from source sentence jointly wit...