A Survey on Dialog Management: Recent Advances and Challenges

• URL: http://arxiv.org/abs/2005.02233v3
• Date: Mon, 25 Oct 2021 11:12:37 GMT
• Title: A Survey on Dialog Management: Recent Advances and Challenges
• Authors: Yinpei Dai, Huihua Yu, Yixuan Jiang, Chengguang Tang, Yongbin Li, Jian Sun
• Abstract summary: Dialog management (DM) is a crucial component in a task-oriented dialog system. Recent advances and challenges within three critical topics for DM: (1) improving model scalability to facilitate dialog system modeling in new scenarios, (2) dealing with the data scarcity problem for dialog policy learning, and (3) enhancing the training efficiency to achieve better task-completion performance.
• Score: 72.52920723074638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the dialog history, DM predicts the dialog state and decides the next action that the dialog agent should take. Recently, dialog policy learning has been widely formulated as a Reinforcement Learning (RL) problem, and more works focus on the applicability of DM. In this paper, we survey recent advances and challenges within three critical topics for DM: (1) improving model scalability to facilitate dialog system modeling in new scenarios, (2) dealing with the data scarcity problem for dialog policy learning, and (3) enhancing the training efficiency to achieve better task-completion performance . We believe that this survey can shed a light on future research in dialog management.

Related papers

• Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents [121.46051697742608]
  We introduce a new dialogue policy planning paradigm to strategize dialogue problems with a tunable language model plug-in named PPDPP. Specifically, we develop a novel training framework to facilitate supervised fine-tuning over available human-annotated data. PPDPP consistently and substantially outperforms existing approaches on three different proactive dialogue applications.
  arXiv  Detail & Related papers  (2023-11-01T03:20:16Z)
• A Mixture-of-Expert Approach to RL-based Dialogue Management [56.08449336469477]
  We use reinforcement learning to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall user satisfaction. Most existing RL approaches to DM train the agent at the word-level, and thus, have to deal with aly complex action space even for a medium-size vocabulary. We develop a RL-based DM using a novel mixture of expert language model (MoE-LM) that consists of (i) a LM capable of learning diverse semantics for conversation histories, (ii) a number of specialized LMs (or experts) capable of generating utterances corresponding to a
  arXiv  Detail & Related papers  (2022-05-31T19:00:41Z)
• "Think Before You Speak": Improving Multi-Action Dialog Policy by Planning Single-Action Dialogs [33.78889030078026]
  Multi-action dialog policy (MADP) generates multiple atomic dialog actions per turn. We propose Planning Enhanced Dialog Policy (PEDP), a novel multi-task learning framework that learns single-action dialog dynamics. Our fully supervised learning-based method achieves a solid task success rate of 90.6%, improving 3% compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-25T07:55:53Z)
• A Survey on Recent Advances and Challenges in Reinforcement LearningMethods for Task-Oriented Dialogue Policy Learning [16.545577313042827]
  Reinforcement Learning (RL) is commonly chosen to learn the dialogue policy, regarding the user as the environment and the system as the agent. In this paper, we survey recent advances and challenges in dialogue policy from the prescriptive of RL. We provide a comprehensive survey of applying RL to dialogue policy learning by categorizing recent methods into basic elements in RL.
  arXiv  Detail & Related papers  (2022-02-28T10:50:22Z)
• UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues [59.499965460525694]
  We propose a unified dialogue system (UniDS) with the two aforementioned skills. We design a unified dialogue data schema, compatible for both chit-chat and task-oriented dialogues. We train UniDS with mixed dialogue data from a pretrained chit-chat dialogue model.
  arXiv  Detail & Related papers  (2021-10-15T11:56:47Z)
• Integrating Pre-trained Model into Rule-based Dialogue Management [32.90885176553305]
  Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems. Data-driven dialogue systems, usually with end-to-end structures, are popular in academic research. We propose a method to leverage the strength of both rule-based and data-driven dialogue managers.
  arXiv  Detail & Related papers  (2021-02-17T03:44:22Z)
• Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management [29.57382819573169]
  We focus on devising a policy that chooses which dialogue action to respond to the user. The sequential system decision-making process can be abstracted into a partially observable Markov decision process. In the past few years, there are many deep reinforcement learning (DRL) algorithms, which use neural networks (NN) as function approximators.
  arXiv  Detail & Related papers  (2020-09-22T05:39:31Z)
• Rethinking Dialogue State Tracking with Reasoning [76.0991910623001]
  This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method significantly outperforms the state-of-the-art methods by 38.6% in terms of joint belief accuracy for MultiWOZ 2.1.
  arXiv  Detail & Related papers  (2020-05-27T02:05:33Z)
• Recent Advances and Challenges in Task-oriented Dialog System [63.82055978899631]
  Task-oriented dialog systems are attracting more and more attention in academic and industrial communities. We discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning, and (3) integrating domain knowledge into the dialog model.
  arXiv  Detail & Related papers  (2020-03-17T01:34:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.