We apply phrase-based and neural models to a core task in interactive machine translation: suggesting how to complete a partial translation.For the phrase-based system, we demonstrate improvements in suggestion quality using novel objective functions, learning techniques, and inference algorithms tailored to this task.Our contributions include new tunable metrics, an improved beam search strategy, an n-best extraction method that increases suggestion diversity, and a tuning procedure for a hierarchical joint model of alignment and translation.The combination of these techniques improves next-word suggestion accuracy dramatically from 28.5% to 41.2% in a large-scale English-German experiment.Our recurrent neural translation system increases accuracy yet further to 53.0%, but inference is two orders of magnitude slower.Manual error analysis shows the strengths and weaknesses of both approaches.

We present an incremental adaptation approach for statistical machine translation that maintains a flexible hierarchical domain structure within a single consistent model.Both weights and rules are updated incrementally on a stream of post-edits.Our multi-level domain hierarchy allows the system to adapt simultaneously towards local context at different levels of granularity, including genres and individual documents.Our experiments show consistent improvements in translation quality from all components of our approach.

Spence Green, Sida I. Wang, Jason Chuang, Jeffrey Heer, Sebastian Schuster, Christopher D. Manning. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014.

The standard approach to computer-aided language translation is post-editing: a machine generates a single translation that a human translator corrects. Recent studies have shown this simple technique to be surprisingly effective, yet it underutilizes the complementary strengths of precision-oriented humans and recall-oriented machines. We present Predictive Translation Memory, an interactive, mixed-initiative system for human language translation. Translators build translations incrementally by considering machine suggestions that update according to the user's current partial translation. In a large-scale study, we find that professional translators are slightly slower in the interactive mode yet produce slightly higher quality translations despite significant prior experience with the baseline post-editing condition. Our analysis identifies significant predictors of time and quality, and also characterizes interactive aid usage. Subjects entered over 99% of characters via interactive aids, a significantly higher fraction than that shown in previous work.

Language translation is slow and expensive, so various forms of machine assistance have been devised. Automatic machine translation systems process text quickly and cheaply, but with quality far below that of skilled human translators. To bridge this quality gap, the translation industry has investigated post-editing, or the manual correction of machine output. We present the first rigorous, controlled analysis of post-editing and find that post-editing leads to reduced time and, surprisingly, improved quality for three diverse language pairs (English to Arabic, French, and German). Our statistical models and visualizations of experimental data indicate that some simple predictors (like source text part of speech counts) predict translation time, and that post-editing results in very different interaction patterns. From these results we distill implications for the design of new language translation interfaces.

Multiword expressions lie at the syntax/semantics interface and have motivated alternative theories of syntax like Construction Grammar. Until now, however, syntactic analysis and multiword expression identification have been modeled separately in natural language processing. We develop two structured prediction models for joint parsing and multiword expression identification. The first is based on context-free grammars and the second uses tree substitution grammars, a formalism that can store larger syntactic fragments. Our experiments show that both models can identify multiword expressions with much higher accuracy than a state-of-the-art system based on word co-occurrence statistics. We experiment with Arabic and French, which both have pervasive multiword expressions. Relative to English, they also have richer morphology, which induces lexical sparsity in finite corpora. To combat this sparsity, we develop a simple factored lexical representation for the context-free parsing model. Morphological analyses are automatically transformed into rich feature tags that are scored jointly with lexical items. This technique, which we call a factored lexicon, improves both standard parsing and multiword expression identification accuracy.