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.

We propose a neural graphical model for parsing natural language sentences into their logical representations. The graphical model is based on hybrid tree structures that jointly represent both sentences and semantics. Learning and decoding are done using efficient dynamic programming algorithms. The model is trained under a discriminative setting, which allows us to incorporate a rich set of features. Hybrid tree structures have shown to achieve state-of-the-art results on standard semantic parsing datasets. In this work, we propose a novel model that incorporates a rich, nonlinear featurization by a feedforward neural network. The error signals are computed with respect to the conditional random fields (CRFs) objective using an inside-outside algorithm, which are then backpropagated to the neural network. We demonstrate that by combining the strengths of the exact global inference in the hybrid tree models and the power of neural networks to extract high level features, our model is able to achieve new state-of-the-art results on standard benchmark datasets across different languages.

In this paper, we investigate case restoration for text without case information.Previous such work operates at the word level.We propose an approach using character-level recurrent neural networks (RNN), which performs competitively compared to language modeling and conditional random fields (CRF) approaches.We further provide quantitative and qualitative analysis on how RNN helps improve truecasing.

Different approaches to high-quality grammatical error correction have been proposed recently, many of which have their own strengths and weaknesses.Most of these approaches are based on classification or statistical machine translation (SMT).In this paper, we propose to combine the output from a classification-based system and an SMT-based system to improve the correction quality.We adopt the system combination technique of Heafield and Lavie (2010).We achieve an F 0.5 score of 39.39% on the test set of the CoNLL-2014 shared task, outperforming the best system in the shared task.