Weiwei Wang


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DiDi’s Machine Translation System for WMT2020
Tanfang Chen | Weiwei Wang | Wenyang Wei | Xing Shi | Xiangang Li | Jieping Ye | Kevin Knight
Proceedings of the Fifth Conference on Machine Translation

This paper describes the DiDi AI Labs’ submission to the WMT2020 news translation shared task. We participate in the translation direction of Chinese->English. In this direction, we use the Transformer as our baseline model and integrate several techniques for model enhancement, including data filtering, data selection, back-translation, fine-tuning, model ensembling, and re-ranking. As a result, our submission achieves a BLEU score of 36.6 in Chinese->English.

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Enhanced Sentence Alignment Network for Efficient Short Text Matching
Zhe Hu | Zuohui Fu | Cheng Peng | Weiwei Wang
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

Cross-sentence attention has been widely applied in text matching, in which model learns the aligned information between two intermediate sequence representations to capture their semantic relationship. However, commonly the intermediate representations are generated solely based on the preceding layers and the models may suffer from error propagation and unstable matching, especially when multiple attention layers are used. In this paper, we pro-pose an enhanced sentence alignment network with simple gated feature augmentation, where the model is able to flexibly integrate both original word and contextual features to improve the cross-sentence attention. Moreover, our model is less complex with fewer parameters compared to many state-of-the-art structures.Experiments on three benchmark datasets validate our model capacity for text matching.