Yutao Zhu


2022

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Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding
Zhaoye Fei | Yu Tian | Yongkang Wu | Xinyu Zhang | Yutao Zhu | Zheng Liu | Jiawen Wu | Dejiang Kong | Ruofei Lai | Zhao Cao | Zhicheng Dou | Xipeng Qiu
Proceedings of the 29th International Conference on Computational Linguistics

Generalized text representations are the foundation of many natural language understanding tasks. To fully utilize the different corpus, it is inevitable that models need to understand the relevance among them. However, many methods ignore the relevance and adopt a single-channel model (a coarse paradigm) directly for all tasks, which lacks enough rationality and interpretation. In addition, some existing works learn downstream tasks by stitches skill block (a fine paradigm), which might cause irrational results due to its redundancy and noise. In this work, we first analyze the task correlation through three different perspectives, , data property, manual design, and model-based relevance, based on which the similar tasks are grouped together. Then, we propose a hierarchical framework with a coarse-to-fine paradigm, with the bottom level shared to all the tasks, the mid-level divided to different groups, and the top-level assigned to each of the tasks. This allows our model to learn basic language properties from all tasks, boost performance on relevant tasks, and reduce the negative impact from irrelevant tasks. Our experiments on 13 benchmark datasets across five natural language understanding tasks demonstrate the superiority of our method.

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Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation
Hanxun Zhong | Zhicheng Dou | Yutao Zhu | Hongjin Qian | Ji-Rong Wen
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Personalized dialogue systems explore the problem of generating responses that are consistent with the user’s personality, which has raised much attention in recent years. Existing personalized dialogue systems have tried to extract user profiles from dialogue history to guide personalized response generation. Since the dialogue history is usually long and noisy, most existing methods truncate the dialogue history to model the user’s personality. Such methods can generate some personalized responses, but a large part of dialogue history is wasted, leading to sub-optimal performance of personalized response generation. In this work, we propose to refine the user dialogue history on a large scale, based on which we can handle more dialogue history and obtain more abundant and accurate persona information. Specifically, we design an MSP model which consists of three personal information refiners and a personalized response generator. With these multi-level refiners, we can sparsely extract the most valuable information (tokens) from the dialogue history and leverage other similar users’ data to enhance personalization. Experimental results on two real-world datasets demonstrate the superiority of our model in generating more informative and personalized responses.

2021

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基于双星型自注意力网络的搜索结果多样化方法(Search Result Diversification Framework Based on Dual Star-shaped Self-Attention Network)
Xubo Qin (秦绪博) | Zhicheng Dou (窦志成) | Yutao Zhu (朱余韬) | Jirong Wen (文继荣)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

相关研究指出,用户提交给搜索引擎的查询通常为短查询。由于自然语言本身的特点,短查询通常具有歧义性,同一个查询可以指代不同的事物,或同一事物的不同方面。为了让搜索结果尽可能满足用户多样化的信息需求,搜索引擎需要对返回的结果进行多样化排序,搜索结果多样化技术应运而生。目前已有的基于全局交互的多样化方法通过全连接的自注意力网络捕获全体候选文档间的交互关系,取得了较好的效果。但由于此类方法只考虑文档间的相关关系,并没有考虑到文档是否具有跟查询相关的有效信息,在训练数据有限的条件下效率相对较低。该文提出了一种基于双星型自注意力网络的搜索结果多样化方法,将全连接结构改为星型拓扑结构,并嵌入查询信息以高效率地提取文档跟查询相关的全局交互特征。相关实验结果显示,该模型相对于基于全连接自注意力网络的多样化方法,具备显著的性能优势。

2020

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ScriptWriter: Narrative-Guided Script Generation
Yutao Zhu | Ruihua Song | Zhicheng Dou | Jian-Yun Nie | Jin Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

It is appealing to have a system that generates a story or scripts automatically from a storyline, even though this is still out of our reach. In dialogue systems, it would also be useful to drive dialogues by a dialogue plan. In this paper, we address a key problem involved in these applications - guiding a dialogue by a narrative. The proposed model ScriptWriter selects the best response among the candidates that fit the context as well as the given narrative. It keeps track of what in the narrative has been said and what is to be said. A narrative plays a different role than the context (i.e., previous utterances), which is generally used in current dialogue systems. Due to the unavailability of data for this new application, we construct a new large-scale data collection GraphMovie from a movie website where end- users can upload their narratives freely when watching a movie. Experimental results on the dataset show that our proposed approach based on narratives significantly outperforms the baselines that simply use the narrative as a kind of context.