Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users

• URL: http://arxiv.org/abs/2310.20479v1
• Date: Tue, 31 Oct 2023 14:12:07 GMT
• Title: Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
• Authors: Yohan Jo, Xinyan Zhao, Arijit Biswas, Nikoletta Basiou, Vincent Auvray, Nikolaos Malandrakis, Angeliki Metallinou, Alexandros Potamianos
• Abstract summary: We release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and one agent. These dialogues reflect interesting dynamics of collaborative decision-making in task-oriented scenarios. We propose a novel task of multi-user contextual query rewriting: to rewrite a task-oriented chat between two users as a concise task-oriented query.
• Score: 51.34484827552774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively. To facilitate development of such systems, we release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and one agent. To collect this dataset, each user utterance from MultiWOZ 2.2 was replaced with a small chat between two users that is semantically and pragmatically consistent with the original user utterance, thus resulting in the same dialogue state and system response. These dialogues reflect interesting dynamics of collaborative decision-making in task-oriented scenarios, e.g., social chatter and deliberation. Supported by this data, we propose the novel task of multi-user contextual query rewriting: to rewrite a task-oriented chat between two users as a concise task-oriented query that retains only task-relevant information and that is directly consumable by the dialogue system. We demonstrate that in multi-user dialogues, using predicted rewrites substantially improves dialogue state tracking without modifying existing dialogue systems that are trained for single-user dialogues. Further, this method surpasses training a medium-sized model directly on multi-user dialogues and generalizes to unseen domains.

Related papers

• Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue [13.774377524019723]
  We propose an efficient Multi-round Interactive Dialogue Tuning (Midi-Tuning) framework. It models the agent and user individually with two adapters built upon large language models. Our framework performs superior to traditional fine-tuning and harbors the tremendous potential for improving dialogue consistency.
  arXiv  Detail & Related papers  (2024-02-10T14:52:52Z)
• Unified Conversational Models with System-Initiated Transitions between Chit-Chat and Task-Oriented Dialogues [4.714297769572548]
  We investigate the potential initiative'' that occurs when there is a change between dialogue modes in one dialogue. We contribute two efficient prompt models which can proactively generate a transition sentence to trigger system-initiated transitions.
  arXiv  Detail & Related papers  (2023-07-04T11:53:23Z)
• Manual-Guided Dialogue for Flexible Conversational Agents [84.46598430403886]
  How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be critical issues in building a task-oriented dialogue system. We propose a novel manual-guided dialogue scheme, where the agent learns the tasks from both dialogue and manuals. Our proposed scheme reduces the dependence of dialogue models on fine-grained domain ontology, and makes them more flexible to adapt to various domains.
  arXiv  Detail & Related papers  (2022-08-16T08:21:12Z)
• User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems [65.88679683468143]
  We propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction. USDA incorporates the sequential transitions of both content and act features in the dialogue to predict the user satisfaction. Experimental results on four benchmark goal-oriented dialogue datasets show that the proposed method substantially and consistently outperforms existing methods on USE.
  arXiv  Detail & Related papers  (2022-02-07T02:50:07Z)
• Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots [62.295373408415365]
  We propose a personalized hybrid matching network (PHMN) for context-response matching. Our contributions are two-fold: 1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information. We evaluate our model on two large datasets with user identification, i.e., personalized dialogue Corpus Ubuntu (P- Ubuntu) and personalized Weibo dataset (P-Weibo)
  arXiv  Detail & Related papers  (2021-03-17T09:42:11Z)
• A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues [22.298425763949634]
  The evaluation of multi-turn dialogues remains challenging. assigning the same experience score to two tasks with different complexity levels is misleading. We develop a new method to estimate the turn-level satisfaction for dialogue, which is context-sensitive and has a long-term view.
  arXiv  Detail & Related papers  (2021-03-01T20:00:28Z)
• UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues [101.96097419995556]
  "UniConv" is a novel unified neural architecture for end-to-end conversational systems in task-oriented dialogues. We conduct comprehensive experiments in dialogue state tracking, context-to-text, and end-to-end settings on the MultiWOZ2.1 benchmark.
  arXiv  Detail & Related papers  (2020-04-29T16:28:22Z)
• Conversation Learner -- A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems [57.082447660944965]
  Conversation Learner is a machine teaching tool for building dialog managers. It enables dialog authors to create a dialog flow using familiar tools, converting the dialog flow into a parametric model. It allows dialog authors to improve the dialog manager over time by leveraging user-system dialog logs as training data.
  arXiv  Detail & Related papers  (2020-04-09T00:10:54Z)

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.