近日，博士生张博关于对话生成方面的研究成果被IEEE旗下Transactions on Audio, Speech and Language Processing（TASLP）期刊录用，其为中科院一区期刊，CCF推荐期刊B类，清华计算机类推荐期刊A类。

  题目：Exploiting Pairwise Mutual Information for Knowledge-Grounded Dialogue（基于两两互信息的知识对话系统）

  摘要：External document knowledge is helpful for dialogue systems to generate high-quality responses. Although several knowledge-grounded dialogue models have been designed, external knowledge cannot be comprehensively exploited due to the complex relationships among dialogue context, knowledge, and responses. To this end, we propose a novel transformer-based model, named TransIKG, which incorporates external document knowledge for dialogue generation. TransIKG comprises a two-step integration mechanism, including correlation integration and overall integration. Correlation integration is designed to fully exploit the pairwise mutual information among dialogue context, knowledge, and responses, while overall integration adopts an integration gate to capture global information. Furthermore, we utilize the positional information of dialogue turns to better represent the dialogue context and enhance the generalization ability of our model on out-of-domain documents. Finally, we propose a novel knowledge-aware pointer network to generate knowledge-enhanced response tokens. Experimental results on two benchmark datasets demonstrate that our model outperforms state-of-the-art models on both open-domain and domain-specific dialogues.

  额外的文档知识有助于对话系统产生高质量的回复。尽管已经存在一些基于知识的对话模型，但由于对话背景、知识和回复之间存在的复杂关系，导致外部知识不能被全面利用。为此，我们提出了一种基于Transformer的新颖模型，名为TransIKG，它结合了外部文档知识进行对话生成。TransIKG有一个两步整合机制，包括相关整合和整体整合。相关整合是为了充分利用对话背景、知识和回复之间的两两互信息，而整体整合则采用了一个整合门来捕获全局信息。此外，我们还利用了对话轮次的位置信息来更好地表示对话内容，并增强了我们的模型在域外文档上的泛化能力。最后，我们提出了一种新的知识感知指针网络，以生成具有知识增强的回复。在两个基准数据集上的实验结果表明，我们的模型在开放领域和特定领域的对话中都优于最先进的模型。