text: In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence.These vectors are generated by parameters which are updated by back-propagation of translation errors through time. We argue that propagating errors through the end-to-end recurrent structures are not a direct way of control the hidden vectors. In this paper, we propose to use word predictions as a mechanism for direct supervision. More specifically, we require these vectors to be able to predict the vocabulary in target sentence. Our simple mechanism ensures better representations in the encoder and decoder without using any extra data or annotation. It is also helpful in reducing the target side vocabulary and improving the decoding efficiency. Experiments on Chinese-English and German-English machine translation tasks show BLEU improvements by 4.53 and 1.3, respectively
  @cite_18: In this work, we propose two extensions of standard word lexicons in statistical machine translation: A discriminative word lexicon that uses sentence-level source information to predict the target words and a trigger-based lexicon model that extends IBM model 1 with a second trigger, allowing for a more fine-grained lexical choice of target words. The models capture dependencies that go beyond the scope of conventional SMT models such as phrase-and language models. We show that the models improve translation quality by 1 in BLEU over a competitive baseline on a large-scale task.
  @cite_4: In order to capture rich language phenomena, neural machine translation models have to use a large vocabulary size, which requires high computing time and large memory usage. In this paper, we alleviate this issue by introducing a sentence-level or batch-level vocabulary, which is only a very small sub-set of the full output vocabulary. For each sentence or batch, we only predict the target words in its sentencelevel or batch-level vocabulary. Thus, we reduce both the computing time and the memory usage. Our method simply takes into account the translation options of each word or phrase in the source sentence, and picks a very small target vocabulary for each sentence based on a wordto-word translation model or a bilingual phrase library learned from a traditional machine translation model. Experimental results on the large-scale English-toFrench task show that our method achieves better translation performance by 1 BLEU point over the large vocabulary neural machine translation system of (2015).
  @cite_3: Translating into morphologically rich languages is a particularly difficult problem in machine translation due to the high degree of inflectional ambiguity in the target language, often only poorly captured by existing word translation models. We present a general approach that exploits source-side contexts of foreign words to improve translation prediction accuracy. Our approach is based on a probabilistic neural network which does not require linguistic annotation nor manual feature engineering. We report significant improvements in word translation prediction accuracy for three morphologically rich target languages. In addition, preliminary results for integrating our approach into a largescale English-Russian statistical machine translation system show small but statistically significant improvements in translation quality.
  @cite_2: Machine translation of a source language sentence involves selecting appropriate target language words and ordering the selected words to form a well-formed target language sentence. Most of the previous work on statistical machine translation relies on (local) associations of target words phrases with source words phrases for lexical selection. In contrast, in this paper, we present a novel approach to lexical selection where the target words are associated with the entire source sentence (global) without the need to compute local associations. Further, we present a technique for reconstructing the target language sentence from the selected words. We compare the results of this approach against those obtained from a finite-state based statistical machine translation system which relies on local lexical associations.
  @cite_15: Classical translation models constrain the space of possible outputs by selecting a subset of translation rules based on the input sentence. Recent work on improving the efficiency of neural translation models adopted a similar strategy by restricting the output vocabulary to a subset of likely candidates given the source. In this paper we experiment with context and embedding-based selection methods and extend previous work by examining speed and accuracy trade-offs in more detail. We show that decoding time on CPUs can be reduced by up to 90 and training time by 25 on the WMT15 English-German and WMT16 English-Romanian tasks at the same or only negligible change in accuracy. This brings the time to decode with a state of the art neural translation system to just over 140 words per seconds on a single CPU core for English-German.
  @cite_16: This paper describes successful applications of discriminative lexicon models to the statistical machine translation (SMT) systems into morphologically complex languages. We extend the previous work on discriminatively trained lexicon models to include more contextual information in making lexical selection decisions by building a single global log-linear model of translation selection. In offline experiments, we show that the use of the expanded contextual information, including morphological and syntactic features, help better predict words in three target languages with complex morphology (Bulgarian, Czech and Korean). We also show that these improved lexical prediction models make a positive impact in the end-to-end SMT scenario from English to these languages.
summary: The word prediction technique has been applied in the research of both statistical machine translation (SMT) @cite_2 @cite_18 @cite_16 @cite_3 and NMT @cite_4 @cite_15 . In these research, word prediction mechanisms are employed to decide the selection of words or constrain the target vocabulary, while in this paper, we use word prediction as a control mechanism for neural model training.