DuTongChuanDuTongChuan, abbreviation of Baidu simultaneous interpreting in Chinese Pinyin. : Context-aware Translation Model for Simultaneous Interpreting
Table 4: The overall results on BSTC Chinese-English translation task (Pre-train represents training on the NIST dataset, and fine-tune represents fine-tuning on the BSTC dataset.). Clean input indicates the input is from human annotated transcription, while the ASR input represents the input contains ASR errors. ASR + Auto IU indicates that the sentence boundary as well as sub-sentence is detected by our IU detector. Therefore, this data basically reflects the real environment in practical product.
['Models', 'Clean Input Pre-train', 'Clean Input Fine-tune', 'ASR Input Pre-train', 'ASR Input Fine-tune', 'ASR + Auto IU Pre-train', 'ASR + Auto IU Fine-tune']
Due to the relatively lower CER in ASR errors (10.32 %), the distinction between the clean input and the noisy input results in a BLEU score difference smaller than 2 points (15.85 vs. 14.60 for pre-train, and 21.98 vs. 19.91 for fine-tune). Despite the small size of the training data in BSTC, fine-tuning on this data is essential to improve the performance of all models. In all settings, the best system in context-aware model beats the wait-15 model. Pre-trained models are not sensitive to errors from Auto IU, while fine-tuned models are.