Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understanding the meaning of the sentence. We propose a method to alleviate this problem by augmenting NMT systems with discrete translation lexicons that efficiently encode translations of these low-frequency words. We describe a method to calculate the lexicon probability of the next word in the translation candidate by using the attention vector of the NMT model to select which source word lexical probabilities the model should focus on. We test two methods to combine this probability with the standard NMT probability: (1) using it as a bias, and (2) linear interpolation. Experiments on two corpora show an improvement of 2.0-2.3 BLEU and 0.13-0.44 NIST score, and faster convergence time.
Finally, there have been a number of recent works that improve accuracy of low-frequency words using character-based translation models @cite_12 @cite_30 @cite_35 . However, have found that even when using character-based models, incorporating information about words allows for gains in translation accuracy, and it is likely that our lexicon-based method could result in improvements in these hybrid systems as well.
@cite_30 @cite_35 @cite_12
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Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understanding the meaning of the sentence. We propose a method to alleviate this problem by augmenting NMT systems with discrete translation lexicons that efficiently encode translations of these low-frequency words. We describe a method to calculate the lexicon probability of the next word in the translation candidate by using the attention vector of the NMT model to select which source word lexical probabilities the model should focus on. We test two methods to combine this probability with the standard NMT probability: (1) using it as a bias, and (2) linear interpolation. Experiments on two corpora show an improvement of 2.0-2.3 BLEU and 0.13-0.44 NIST score, and faster convergence time. Since Japanese and Chinese languages have too many characters to be input directly using a standard keyboard, input methods for these languages that enable users to input the characters are required. Recently, input methods based on statistical models have become popular because of their accuracy and ease of maintenance. Most of them adopt word-based models because they utilize word-segmented corpora to train the models. However, such word-based models suffer from unknown words because they cannot convert words correctly which are not in corpora. To handle this problem, we propose a character-based model that enables input methods to convert unknown words by exploiting character-aligned corpora automatically generated by a monotonic alignment tool. In addition to the character-based model, we propose an ensemble model of both character-based and word-based models to achieve higher accuracy. The ensemble model combines these two models by linear interpolation. All of these models are based on joint source channel model to utilize rich context through higher order joint n-gram. Experiments on Japanese and Chinese datasets showed that the character-based model performs reasonably and the ensemble model outperforms the word-based baseline model. As a future work, the effectiveness of incorporating large raw data should be investigated. This paper describes improved HMM-based word level alignment models for statistical machine translation. We present a method for using part of speech tag information to improve alignment accuracy, and an approach to modeling fertility and correspondence to the empty word in an HMM alignment model. We present accuracy results from evaluating Viterbi alignments against human-judged alignments on the Canadian Hansards corpus, as compared to a bigram HMM, and IBM model 4. The results show up to 16 alignment error reduction. Nearly all previous work on neural machine translation (NMT) has used quite restricted vocabularies, perhaps with a subsequent method to patch in unknown words. This paper presents a novel word-character solution to achieving open vocabulary NMT. We build hybrid systems that translate mostly at the word level and consult the character components for rare words. Our character-level recurrent neural networks compute source word representations and recover unknown target words when needed. The twofold advantage of such a hybrid approach is that it is much faster and easier to train than character-based ones; at the same time, it never produces unknown words as in the case of word-based models. On the WMT'15 English to Czech translation task, this hybrid approach offers an addition boost of +2.1-11.4 BLEU points over models that already handle unknown words. Our best system achieves a new state-of-the-art result with 20.7 BLEU score. We demonstrate that our character models can successfully learn to not only generate well-formed words for Czech, a highly-inflected language with a very complex vocabulary, but also build correct representations for English source words.