Lexically Constrained Neural Machine Translation with Levenshtein Transformer

Raymond Susanto, Shamil Chollampatt, Liling Tan

Abstract:

This paper proposes a simple and effective algorithm for incorporating lexical constraints in
neural machine translation. Previous work either required re-training existing models with
the lexical constraints or incorporating them
during beam search decoding with significantly higher computational overheads. Leveraging the flexibility and speed of a recently
proposed Levenshtein Transformer model (Gu
et al., 2019), our method injects terminology
constraints at inference time without any impact on decoding speed. Our method does
not require any modification to the training
procedure and can be easily applied at runtime with custom dictionaries. Experiments on
English-German WMT datasets show that our
approach improves an unconstrained baseline
and previous approaches.