生成目标导向型主动对话的目标约束双向规划

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jian Wang, Dongding Lin, Wenjie Li
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引用次数: 0

摘要

以目标为导向的主动对话系统旨在将对话从对话情境引向预先确定的目标,如就指定项目提出建议或引入新的特定话题。为此,这类对话系统必须规划合理的行动来主动推动对话,同时规划适当的话题来推动对话顺利进入目标话题。在这项工作中,我们主要关注面向目标对话生成的有效对话规划。受认知科学决策理论的启发,我们提出了一种新颖的目标受限双向规划(TRIP)方法,通过前瞻和后顾之忧来规划合适的对话路径。通过将规划制定为一项生成任务,我们的 TRIP 利用两个变换器解码器双向生成对话路径,该路径由一系列动作、话题对组成。我们希望这两个解码器能相互监督,并通过最小化决策差距和目标的对比生成来趋同于一致的行动和话题。此外,我们还提出了一种具有双向协议的目标受限解码算法,以更好地控制计划过程。随后,我们采用规划好的对话路径,以流水线方式指导对话生成,并探索了两种变体:基于提示的生成和计划控制的生成。我们在两个具有挑战性的对话数据集上进行了广泛的实验,这些数据集被重新用于探索面向目标的对话。我们的自动和人工评估结果表明,所提出的方法明显优于各种基线模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue

Target-oriented proactive dialogue systems aim to lead conversations from a dialogue context toward a pre-determined target, such as making recommendations on designated items or introducing new specific topics. To this end, it is critical for such dialogue systems to plan reasonable actions to drive the conversation proactively, and meanwhile, to plan appropriate topics to move the conversation forward to the target topic smoothly. In this work, we mainly focus on effective dialogue planning for target-oriented dialogue generation. Inspired by decision-making theories in cognitive science, we propose a novel target-constrained bidirectional planning (TRIP) approach, which plans an appropriate dialogue path by looking ahead and looking back. By formulating the planning as a generation task, our TRIP bidirectionally generates a dialogue path consisting of a sequence of <action, topic> pairs using two Transformer decoders. They are expected to supervise each other and converge on consistent actions and topics by minimizing the decision gap and contrastive generation of targets. Moreover, we propose a target-constrained decoding algorithm with a bidirectional agreement to better control the planning process. Subsequently, we adopt the planned dialogue paths to guide dialogue generation in a pipeline manner, where we explore two variants: prompt-based generation and plan-controlled generation. Extensive experiments are conducted on two challenging dialogue datasets, which are re-purposed for exploring target-oriented dialogue. Our automatic and human evaluations demonstrate that the proposed methods significantly outperform various baseline models.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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