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.
Beyond the research on translation models, there are many research on the other relevant problems, such as sentence boundary detection for realtime speech translation @cite_21 @cite_5 @cite_26 @cite_11 @cite_6 , low-latency simultaneous interpreting @cite_31 @cite_22 @cite_7 @cite_28 @cite_3 @cite_41 @cite_36 , automatic punctuation annotation for speech transcription @cite_37 @cite_13 , and discussion about human and machine in simultaneous interpreting @cite_18 .
@cite_37 @cite_26 @cite_18 @cite_22 @cite_7 @cite_28 @cite_41 @cite_36 @cite_21 @cite_6 @cite_3 @cite_5 @cite_31 @cite_13 @cite_11
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For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models' ability to exploit previous source and target sentences. We investigate the performance of recently proposed multi-encoder NMT models trained on subtitles for English to French. We also explore a novel way of exploiting context from the previous sentence. Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50 accuracy on our coreference test set and 53.5 for coherence cohesion (compared to a non-contextual baseline of 50 ). A simple strategy of decoding the concatenation of the previous and current sentence leads to good performance, and our novel strategy of multi-encoding and decoding of two sentences leads to the best performance (72.5 for coreference and 57 for coherence cohesion), highlighting the importance of target-side context. Speech translation is usually the pipeline task of automatic speech recognition (ASR), translation unit segmentation and machine translation (MT). Segmenting the ASR output to translation units poses a challenge of balancing the translation quality and efficiency for real-time speech translation. In this paper, we firstly propose a parser-based semantic boundary detection method to detect all semantic boundaries based on our definition. To realize the translation of the semantic units, a word-boundary language model is secondly proposed to improve the translation quality. Experiments on English to Chinese and Chinese to English speech translation have shown that the proposed method yields improved translation quality and lower latency, when compared to the conventional punctuated methods. Corpus-based approaches to machine translation (MT) rely on the availability of parallel corpora. To produce user-acceptable translation outputs, such systems need high quality data to be efficiently trained, optimized and evaluated. However, building high quality dataset is a relatively expensive task. In this paper, we describe the data collection and analysis of a large database of 10.881 SMT translation output hypotheses manually corrected. These post-editions were collected using Amazon’s Mechanical Turk, following some ethical guidelines. A complete analysis of the collected data pointed out a high quality of the corrections with more than 87 of the collected post-editions that improve hypotheses and more than 94 of the crowdsourced post-editions which are at least of professional quality. We also post-edited 1,500 gold-standard reference translations (of bilingual parallel corpora generated by professional) and noticed that 72 of these translations needed to be corrected during post-edition. We computed a proximity measure between the different kind of translations and pointed out that reference translations are as far from the hypotheses as from the corrected hypotheses (i.e. the post-editions). In light of these last findings, we discuss the adequation of text-based generated reference translations to train setence-to-sentence based SMT systems. We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no change in the model architecture from our base system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. The rest of the model, which includes encoder, decoder and attention, remains unchanged and is shared across all languages. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model without any increase in parameters, which is significantly simpler than previous proposals for Multilingual NMT. Our method often improves the translation quality of all involved language pairs, even while keeping the total number of model parameters constant. On the WMT'14 benchmarks, a single multilingual model achieves comparable performance for English @math French and surpasses state-of-the-art results for English @math German. Similarly, a single multilingual model surpasses state-of-the-art results for French @math English and German @math English on WMT'14 and WMT'15 benchmarks respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and show some interesting examples when mixing languages.