Tingxun Shi


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Manifold’s English-Chinese System at WMT22 General MT Task
Chang Jin | Tingxun Shi | Zhengshan Xue | Xiaodong Lin
Proceedings of the Seventh Conference on Machine Translation (WMT)

Manifold’s English-Chinese System at WMT22 is an ensemble of 4 models trained by different configurations with scheduled sampling-based fine-tuning. The four configurations are DeepBig (XenC), DeepLarger (XenC), DeepBig-TalkingHeads (XenC) and DeepBig (LaBSE). Concretely, DeepBig extends Transformer-Big to 24 encoder layers. DeepLarger has 20 encoder layers and its feed-forward network (FFN) dimension is 8192. TalkingHeads applies the talking-heads trick. For XenC configs, we selected monolingual and parallel data that is similar to the past newstest datasets using XenC, and for LaBSE, we cleaned the officially provided parallel data using LaBSE pretrained model. According to the officially released autonomic metrics leaderboard, our final constrained system ranked 1st among all others when evaluated by bleu-all, chrf-all and COMET-B, 2nd by COMET-A.


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OPPO’s Machine Translation Systems for WMT20
Tingxun Shi | Shiyu Zhao | Xiaopu Li | Xiaoxue Wang | Qian Zhang | Di Ai | Dawei Dang | Xue Zhengshan | Jie Hao
Proceedings of the Fifth Conference on Machine Translation

In this paper we demonstrate our (OPPO’s) machine translation systems for the WMT20 Shared Task on News Translation for all the 22 language pairs. We will give an overview of the common aspects across all the systems firstly, including two parts: the data preprocessing part will show how the data are preprocessed and filtered, and the system part will show our models architecture and the techniques we followed. Detailed information, such as training hyperparameters and the results generated by each technique will be depicted in the corresponding subsections. Our final submissions ranked top in 6 directions (English Czech, English Russian, French German and Tamil English), third in 2 directions (English German, English Japanese), and fourth in 2 directions (English Pashto and and English Tamil).

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OPPO’s Machine Translation System for the IWSLT 2020 Open Domain Translation Task
Qian Zhang | Xiaopu Li | Dawei Dang | Tingxun Shi | Di Ai | Zhengshan Xue | Jie Hao
Proceedings of the 17th International Conference on Spoken Language Translation

In this paper, we demonstrate our machine translation system applied for the Chinese-Japanese bidirectional translation task (aka. open domain translation task) for the IWSLT 2020. Our model is based on Transformer (Vaswani et al., 2017), with the help of many popular, widely proved effective data preprocessing and augmentation methods. Experiments show that these methods can improve the baseline model steadily and significantly.