Di Wu


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Context-Aware Cross-Attention for Non-Autoregressive Translation
Liang Ding | Longyue Wang | Di Wu | Dacheng Tao | Zhaopeng Tu
Proceedings of the 28th International Conference on Computational Linguistics

Non-autoregressive translation (NAT) significantly accelerates the inference process by predicting the entire target sequence. However, due to the lack of target dependency modelling in the decoder, the conditional generation process heavily depends on the cross-attention. In this paper, we reveal a localness perception problem in NAT cross-attention, for which it is difficult to adequately capture source context. To alleviate this problem, we propose to enhance signals of neighbour source tokens into conventional cross-attention. Experimental results on several representative datasets show that our approach can consistently improve translation quality over strong NAT baselines. Extensive analyses demonstrate that the enhanced cross-attention achieves better exploitation of source contexts by leveraging both local and global information.

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SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling
Di Wu | Liang Ding | Fan Lu | Jian Xie
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system. In this paper, we propose a novel non-autoregressive model named SlotRefine for joint intent detection and slot filling. Besides, we design a novel two-pass iteration mechanism to handle the uncoordinated slots problem caused by conditional independence of non-autoregressive model. Experiments demonstrate that our model significantly outperforms previous models in slot filling task, while considerably speeding up the decoding (up to x10.77). In-depth analysis show that 1) pretraining schemes could further enhance our model; 2) two-pass mechanism indeed remedy the uncoordinated slots.


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WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
Yufeng Diao | Hongfei Lin | Di Wu | Liang Yang | Kan Xu | Zhihao Yang | Jian Wang | Shaowu Zhang | Bo Xu | Dongyu Zhang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Homographic puns have a long history in human writing, widely used in written and spoken literature, which usually occur in a certain syntactic or stylistic structure. How to recognize homographic puns is an important research. However, homographic pun recognition does not solve very well in existing work. In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns. Our experiments on the SemEval2017 Task7 and Pun of the Day demonstrate that the proposed model is able to distinguish between homographic pun and non-homographic pun texts. We show the effectiveness of the model to present the capability of choosing qualitatively informative words. The results show that our model achieves the state-of-the-art performance on homographic puns recognition.