In this paper, we present DuTongChuan, a novel context-aware translation model for simultaneous interpreting. This model allows to constantly read streaming text from the Automatic Speech Recognition (ASR) model and simultaneously determine the boundaries of Information Units (IUs) one after another. The detected IU is then translated into a fluent translation with two simple yet effective decoding strategies: partial decoding and context-aware decoding. In practice, by controlling the granularity of IUs and the size of the context, we can get a good trade-off between latency and translation quality easily. Elaborate evaluation from human translators reveals that our system achieves promising translation quality (85.71 for Chinese-English, and 86.36 for English-Chinese), specially in the sense of surprisingly good discourse coherence. According to an End-to-End (speech-to-speech simultaneous interpreting) evaluation, this model presents impressive performance in reducing latency (to less than 3 seconds at most times). Furthermore, we successfully deploy this model in a variety of Baidu's products which have hundreds of millions of users, and we release it as a service in our AI platform.
Many studies have proposed to synthesize realistic ASR errors, and augment them with translation training data, to enhance the robustness of the NMT model towards ASR errors @cite_15 @cite_35 @cite_42 . However, most of these approaches depend on simple heuristic rules and only evaluate on artificially noisy test set, which do not always reflect the real noises distribution on training and inference @cite_25 @cite_24 @cite_23 .
@cite_35 @cite_42 @cite_24 @cite_23 @cite_15 @cite_25
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Although neural machine translation (NMT) has achieved impressive progress recently, it is usually trained on the clean parallel data set and hence cannot work well when the input sentence is the production of the automatic speech recognition (ASR) system due to the enormous errors in the source. To solve this problem, we propose a simple but effective method to improve the robustness of NMT in the case of speech translation. We simulate the noise existing in the realistic output of the ASR system and inject them into the clean parallel data so that NMT can work under similar word distributions during training and testing. Besides, we also incorporate the Chinese Pinyin feature which is easy to get in speech translation to further improve the translation performance. Experiment results show that our method has a more stable performance and outperforms the baseline by an average of 3.12 BLEU on multiple noisy test sets, even while achieves a generalization improvement on the WMT'17 Chinese-English test set. Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation (NMT) models. In this paper, we propose to improve the robustness of NMT models with adversarial stability training. The basic idea is to make both the encoder and decoder in NMT models robust against input perturbations by enabling them to behave similarly for the original input and its perturbed counterpart. Experimental results on Chinese-English, English-German and English-French translation tasks show that our approaches can not only achieve significant improvements over strong NMT systems but also improve the robustness of NMT models. Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the homophone noise, where words are replaced by other words with the same (or similar) pronunciations. Homophone noise arises frequently from many real-world scenarios upstream to translation, such as automatic speech recognition (ASR) or phonetic-based input systems. We propose to improve the robustness of NMT to homophone noise by 1) jointly embedding both textual and phonetic information of source sentences, and 2) augmenting the training dataset with homophone noise. Interestingly, we found that in order to achieve the best translation quality, most (though not all) weights should be put on the phonetic rather than textual information, where the latter is only used as auxiliary information. Experiments show that our method not only significantly improves the robustness of NMT to homophone noise, which is expected but also surprisingly improves the translation quality on clean test sets. In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT). A target phrase learned with statistical MT models extends a hypothesis in the NMT beam search when the attention of the NMT model focuses on the source words translated by this phrase. Phrases added in this way are scored with the NMT model, but also with SMT features including phrase-level translation probabilities and a target language model. Experimental results on German->English news domain and English->Russian e-commerce domain translation tasks show that using phrase-based models in NMT search improves MT quality by up to 2.3 BLEU absolute as compared to a strong NMT baseline.