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.
Focus on the simultaneous translation task, there are some work referring to the construction of the simultaneous interpreting corpus @cite_39 @cite_27 @cite_16 . Particularly, @cite_16 deliver a collection of a simultaneous translation corpus for comparative analysis on Japanese-English and English-Japanese speech translation. This work analyze the difference between the translation and the interpretations, using the interpretations from human simultaneous interpreters.
@cite_27 @cite_16 @cite_39
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This paper describes the collection of an English-Japanese Japanese-English simultaneous interpretation corpus. There are two main features of the corpus. The first is that professional simultaneous interpreters with different amounts of experience cooperated with the collection. By comparing data from simultaneous interpretation of each interpreter, it is possible to compare better interpretations to those that are not as good. The second is that for part of our corpus there are already translation data available. This makes it possible to compare translation data with simultaneous interpretation data. We recorded the interpretations of lectures and news, and created time-aligned transcriptions. A total of 387k words of transcribed data were collected. The corpus will be helpful to analyze differences in interpretations styles and to construct simultaneous interpretation systems. Speech-to-speech translation has been an important research topic with the advance of technologies for speech processing and language processing. This paper describes a bilingual speech dialogue corpus which has been constructed for research on simultaneous machine interpretation at the Center for Integrated Acoustic Information Research (CIAIR), Nagoya University. The corpus has been implemented by collecting simulated cross-lingual conversations between English speech and Japanese speech through simultaneous interpretation, and by transcribing them manually with bilingual sentence alignment. In the year 2002, 216 spoken dialogues have been collected under a real environment, and transcribed into text files consisting of about 300,000 morphemes. In order to utilize the bilingual corpus effectively, every source utterance speech has been segmented into interpreting units according to its word-for-word translation and the word alignment of them. The interpreting unit means a linguistic chunk that could be interpreted separately and simultaneously. This paper has investigated linguistic characters of such the unit, and examined the feasibility of simultaneous machine interpretation. This paper describes the design, analysis and utilization of a simultaneous interpretation corpus. The corpus has been constructed at the Center for Integrated Acoustic Information Research (CIAIR) of Nagoya University in order to promote the realization of the multilingual communication supporting environment. The discourse tag and the utterance time tag were given to the corpus. Therefore, the corpus is expected to be useful not only for the development of simultaneous interpreting systems but also for the construction of an interpreting theory. Empirical research on simultaneous interpreting is hampered by the problem of collecting sufficient material (recordings of source speeches and interpreted target speeches) for the testing of hypotheses and validation of existing theories. In other words, corpora have long been awaited in the field of Interpreting Studies. In January 2004 a research group on corpus-based interpreting studies was set up in the Department of Interdiscip linary Studies in Translation, Languages and Cultures (SITLeC) of the University of Bologna at F orli, in order to create an electronic parallel corpus with source and target speeches in Italian, English and Spanish. The main research interest of the group is the study of interpreters’ strategi es across different language directions (directionality) and language-pair related difficul ties. In this paper, the authors illustrate the various stages of development of the EPIC corpus, h ighlighting both research and pedagogical applications of this “multidimensional” tool compri sing video, audio and written materials, which can be retrieved, selected and analyzed by using co rpus linguistics techniques. 2