Speaker or Listener? The Role of a Dialog Agent

Liu, Yafei; Qian, Hongjin; Xu, Hengpeng; Wei, Jinmao

doi:10.18653/v1/2020.findings-emnlp.437

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Personalized Dialogue Generation Opendomain dialogue generation has been extensively studied (Koehn et al, 2003;Vinyals and Le, 2015;Serban et al, 2016;Zhang et al, 2019a,b;Liu et al, 2020;Xiao et al, 2020;Zhu et al, 2020). Recently, personalized dialogue systems have attached more and more attention.…”

Section: Related Workmentioning

confidence: 99%

Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation

Zhong¹,

Dou²,

Zhu³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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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.

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Section: Related Workmentioning

confidence: 99%

Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation

Zhong¹,

Dou²,

Zhu³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

Self Cite

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“…Some exploratory works attempt to empower dialogue systems with the ability to engage in proactive dialogue. For example, Liu et al [100] propose a Initiative-Imitate model to force the dialogue agent to selectively act as a speaker or listener, thus achieving the flexible transition between proactive topic raising and passive interaction during conversation. VAD should demonstrate the capability of cognitive guidance through proactive dialogue under cross-modal dialogue context to achieve target guidance goals, such as affective guidance, opinion guidance, etc.…”

Section: Proactive Dialogue Guidance In Vadmentioning

confidence: 99%

Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review

Wang¹,

Guo²,

Zeng³

et al. 2022

Preprint

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The intelligent dialogue system, aiming at communicating with humans harmoniously with natural language, is brilliant for promoting the advancement of human-machine interaction in the era of artificial intelligence. With the gradually complex human-computer interaction requirements (e.g., multimodal inputs, time sensitivity), it is difficult for traditional text-based dialogue system to meet the demands for more vivid and convenient interaction. Consequently, Visual-Context Augmented Dialogue System (VAD), which has the potential to communicate with humans by perceiving and understanding multimodal information (i.e., visual context in images or videos, textual dialogue history), has become a predominant research paradigm. Benefiting from the consistency and complementarity between visual and textual context, VAD possesses the potential to generate engaging and context-aware responses. For depicting the development of VAD, we first characterize the concepts and unique features of VAD, and then present its generic system architecture to illustrate the system workflow. Subsequently, several research challenges and representative works are detailed investigated, followed by the summary of authoritative benchmarks. We conclude this paper by putting forward some open issues and promising research trends for VAD, e.g., the cognitive mechanisms of human-machine dialogue under cross-modal dialogue context, and knowledge-enhanced cross-modal semantic interaction.CCS Concepts: • Human-centered computing → HCI theory, concepts and models; • Computing methodologies → Discourse, dialogue and pragmatics.

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“…Inconsistent personality is one of the most mentioned challenges of current open-domain chatbots [7,35]. In the dialogue chatbot domain, personality refers to a set of traits that define a chatbot's interaction style, character, and behaviors [7,18]. Inconsistent personality brings more unpredictability and untrustworthiness to the chatbot, which can disorient end users and lead to a strong sense of discomfort [7,29].…”

Section: Introductionmentioning

confidence: 99%

Learning Implicit User Profile for Personalized Retrieval-Based Chatbot

Qian

Dou

Zhu

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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In this paper, we explore the problem of developing personalized chatbots. A personalized chatbot is designed as a digital chatting assistant for a user. The key characteristic of a personalized chatbot is that it should have a consistent personality with the corresponding user. It can talk the same way as the user when it is delegated to respond to others' messages. Many methods have been proposed to assign a personality to dialogue chatbots, but most of them utilize explicit user profiles, including several persona descriptions or key-value-based personal information. In a practical scenario, however, users might be reluctant to write detailed persona descriptions, and obtaining a large number of explicit user profiles requires tremendous manual labour. To tackle the problem, we present a retrieval-based personalized chatbot model, namely IMPChat, to learn an implicit user profile from the user's dialogue history. We argue that the implicit user profile is superior to the explicit user profile regarding accessibility and flexibility. IMPChat aims to learn an implicit user profile through modeling user's personalized language style and personalized preferences separately. To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses; To model a user's personalized preferences, we explore the conditional relations underneath each post-response pair of the user. The personalized preferences are dynamic and context-aware: we assign higher weights to those historical pairs that are topically related to the current query when aggregating the personalized preferences. We match each response candidate with the personalized language style and personalized preference, respectively, and fuse the two matching signals to determine the final ranking score. We conduct comprehensive experiments on two large datasets, and the results show that our method outperforms all baseline models. The codes and datasets will be released at Github 1 .

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Speaker or Listener? The Role of a Dialog Agent

Cited by 4 publications

References 18 publications

Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation

Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation

Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review

Learning Implicit User Profile for Personalized Retrieval-Based Chatbot

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