Xin Wu


TSDG: Content-aware Neural Response Generation with Two-stage Decoding Process
Junsheng Kong | Zhicheng Zhong | Yi Cai | Xin Wu | Da Ren
Findings of the Association for Computational Linguistics: EMNLP 2020

Neural response generative models have achieved remarkable progress in recent years but tend to yield irrelevant and uninformative responses. One of the reasons is that encoder-decoder based models always use a single decoder to generate a complete response at a stroke. This tends to generate high-frequency function words with less semantic information rather than low-frequency content words with more semantic information. To address this issue, we propose a content-aware model with two-stage decoding process named Two-stage Dialogue Generation (TSDG). We separate the decoding process of content words and function words so that content words can be generated independently without the interference of function words. Experimental results on two datasets indicate that our model significantly outperforms several competitive generative models in terms of automatic and human evaluation.

Task-oriented Domain-specific Meta-Embedding for Text Classification
Xin Wu | Yi Cai | Yang Kai | Tao Wang | Qing Li
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Meta-embedding learning, which combines complementary information in different word embeddings, have shown superior performances across different Natural Language Processing tasks. However, domain-specific knowledge is still ignored by existing meta-embedding methods, which results in unstable performances across specific domains. Moreover, the importance of general and domain word embeddings is related to downstream tasks, how to regularize meta-embedding to adapt downstream tasks is an unsolved problem. In this paper, we propose a method to incorporate both domain-specific and task-oriented information into meta-embeddings. We conducted extensive experiments on four text classification datasets and the results show the effectiveness of our proposed method.


HCAMiner: Mining Concept Associations for Knowledge Discovery through Concept Chain Queries
Wei Jin | Xin Wu
Coling 2010: Demonstrations