Hongbin Wang

Also published as: HongBin Wang


2022

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基于注意力的蒙古语说话人特征提取方法(Attention based Mongolian Speaker Feature Extraction)
Fangyuan Zhu (朱方圆) | Zhiqiang Ma (马志强) | Zhiqiang Liu (刘志强) | Caijilahu Bao (宝财吉拉呼) | Hongbin Wang (王洪彬)
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“说话人特征提取模型提取到的说话人特征之间区分性低,使得蒙古语声学模型无法学习到区分性信息,导致模型无法适应不同说话人。提出一种基于注意力的说话人自适应方法,方法引入神经图灵机进行自适应,增加记忆模块存放说话人特征,采用注意力机制计算记忆模块中说话人特征与当前语音说话人特征的相似权重矩阵,通过权重矩阵重新组合成说话人特征s-vector,进而提高说话人特征之间的区分性。在IMUT-MCT数据集上,进行说话人特征提取方法的消融实验、模型自适应实验和案例分析。实验结果表明,对比不同说话人特征s-vector、i-vector与d-vector,s-vector比其他两种方法的SER和WER分别降低4.96%、1.08%;在不同的蒙古语声学模型上进行比较,提出的方法相对于基线均有性能提升。”

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Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems
Ling.Yu Zhu | Zhengkun Zhang | Jun Wang | Hongbin Wang | Haiying Wu | Zhenglu Yang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Empathetic dialogue assembles emotion understanding, feeling projection, and appropriate response generation. Existing work for empathetic dialogue generation concentrates on the two-party conversation scenario. Multi-party dialogues, however, are pervasive in reality. Furthermore, emotion and sensibility are typically confused; a refined empathy analysis is needed for comprehending fragile and nuanced human feelings. We address these issues by proposing a novel task called Multi-Party Empathetic Dialogue Generation in this study. Additionally, a Static-Dynamic model for Multi-Party Empathetic Dialogue Generation, SDMPED, is introduced as a baseline by exploring the static sensibility and dynamic emotion for the multi-party empathetic dialogue learning, the aspects that help SDMPED achieve the state-of-the-art performance.

2021

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A Dialogue-based Information Extraction System for Medical Insurance Assessment
Shuang Peng | Mengdi Zhou | Minghui Yang | Haitao Mi | Shaosheng Cao | Zujie Wen | Teng Xu | Hongbin Wang | Lei Liu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Incorporating Circumstances into Narrative Event Prediction
Shichao Wang | Xiangrui Cai | HongBin Wang | Xiaojie Yuan
Findings of the Association for Computational Linguistics: EMNLP 2021

The narrative event prediction aims to predict what happens after a sequence of events, which is essential to modeling sophisticated real-world events. Existing studies focus on mining the inter-events relationships while ignoring how the events happened, which we called circumstances. With our observation, the event circumstances indicate what will happen next. To incorporate event circumstances into the narrative event prediction, we propose the CircEvent, which adopts the two multi-head attention to retrieve circumstances at the local and global levels. We also introduce a regularization of attention weights to leverage the alignment between events and local circumstances. The experimental results demonstrate our CircEvent outperforms existing baselines by 12.2%. The further analysis demonstrates the effectiveness of our multi-head attention modules and regularization.