In this paper, we discuss different methods which use meta information and richer context that may accompany source language input to improve machine translation quality. We focus on category information of input text as meta information, but the proposed methods can be extended to all textual and non-textual meta information that might be available for the input text or automatically predicted using the text content. The main novelty of this work is to use state-of-the-art neural network methods to tackle this problem within a statistical machine translation (SMT) framework. We observe translation quality improvements up to 3 in terms of BLEU score in some text categories.
Phrase-based MT models have an intrinsic problem in using large(r) context and long range dependencies, since these models are bounded by phrase context. Therefore, many research publications target solving these well-known problems of phrase-based MT. Here, we focus on pioneering works that are most comparable to this work. One research line of using larger context in MT is to use sentence context for word sense disambiguation . proposed the idea of employing a discriminative lexical model that uses sentence level information to predict a target word, this idea has been extended and enhanced in a recent work . @cite_0 have proposed to extend the discriminative model to also use the target prefix to predict the next target word, and also they enhance the model to calculate target word probabilities on-line during the search using a fast and efficient classification method based on the Vowpal Wabbit http: hunch.net vw toolkit. proposed a neural joint lexical model that also employs a larger context around a given source word including previous generated target words, to predict the corresponding target word probability using a feed-forward neural network as the classifier.
@cite_0
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Discriminative translation models utilizing source context have been shown to help statistical machine translation performance. We propose a novel extension of this work using target context information. Surprisingly, we show that this model can be efficiently integrated directly in the decoding process. Our approach scales to large training data sizes and results in consistent improvements in translation quality on four language pairs. We also provide an analysis comparing the strengths of the baseline source-context model with our extended source-context and target-context model and we show that our extension allows us to better capture morphological coherence. Our work is freely available as part of Moses.