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.

Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing effort.APE has had notable success when used with statistical machine translation (SMT) systems but has not been as successful over neural machine translation (NMT) systems.This has raised questions on the relevance of APE task in the current scenario.However, the training of APE models has been heavily reliant on large-scale artificial corpora combined with only limited human post-edited data.We hypothesize that APE models have been underperforming in improving NMT translations due to the lack of adequate supervision.To ascertain our hypothesis, we compile a larger corpus of human post-edits of English to German NMT.We empirically show that a state-of-art neural APE model trained on this corpus can significantly improve a strong in-domain NMT system, challenging the current understanding in the field.We further investigate the effects of varying training data sizes, using artificial training data, and domain specificity for the APE task.

Word clusters are useful for many NLP tasks including training neural network language models, but current increases in datasets are outpacing the ability of word clusterers to handle them.Little attention has been paid thus far on inducing high-quality word clusters at a large scale.The predictive exchange algorithm is quite scalable, but sometimes does not provide as good perplexity as other slower clustering algorithms.We introduce the bidirectional, interpolated, refining, and alternating (BIRA) predictive exchange algorithm.It improves upon the predictive exchange algorithm's perplexity by up to 18%, giving it perplexities comparable to the slower two-sided exchange algorithm, and better perplexities than the slower Brown clustering algorithm.Our BIRA implementation is fast, clustering a 2.5 billion token English News Crawl corpus in 3 hours.It also reduces machine translation training time while preserving translation quality.Our implementation is portable and freely available.