DARD: A Multi-Agent Approach for Task-Oriented Dialog Systems

• URL: http://arxiv.org/abs/2411.00427v1
• Date: Fri, 01 Nov 2024 07:50:19 GMT
• Title: DARD: A Multi-Agent Approach for Task-Oriented Dialog Systems
• Authors: Aman Gupta, Anirudh Ravichandran, Ziji Zhang, Swair Shah, Anurag Beniwal, Narayanan Sadagopan,
• Abstract summary: We propose DARD (Domain Assigned Response Delegation), a multi-agent conversational system capable of handling multi-domain dialogs. DARD leverages domain-specific agents, orchestrated by a central dialog manager agent. We evaluate DARD using the well-established MultiWOZ benchmark, achieving state-of-the-art performance by improving the dialogue inform rate by 6.6% and the success rate by 4.1% over the best-performing existing approaches.
• Score: 1.9145666574544524
• License:
• Abstract: Task-oriented dialogue systems are essential for applications ranging from customer service to personal assistants and are widely used across various industries. However, developing effective multi-domain systems remains a significant challenge due to the complexity of handling diverse user intents, entity types, and domain-specific knowledge across several domains. In this work, we propose DARD (Domain Assigned Response Delegation), a multi-agent conversational system capable of successfully handling multi-domain dialogs. DARD leverages domain-specific agents, orchestrated by a central dialog manager agent. Our extensive experiments compare and utilize various agent modeling approaches, combining the strengths of smaller fine-tuned models (Flan-T5-large & Mistral-7B) with their larger counterparts, Large Language Models (LLMs) (Claude Sonnet 3.0). We provide insights into the strengths and limitations of each approach, highlighting the benefits of our multi-agent framework in terms of flexibility and composability. We evaluate DARD using the well-established MultiWOZ benchmark, achieving state-of-the-art performance by improving the dialogue inform rate by 6.6% and the success rate by 4.1% over the best-performing existing approaches. Additionally, we discuss various annotator discrepancies and issues within the MultiWOZ dataset and its evaluation system.

Related papers

• DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever [83.33209603041013]
  We propose a parameter-efficient prompt-tuning method named DialCLIP for multi-modal dialog retrieval. Our approach introduces a multi-modal context generator to learn context features which are distilled into prompts within the pre-trained vision-language model CLIP. To facilitate various types of retrieval, we also design multiple experts to learn mappings from CLIP outputs to multi-modal representation space.
  arXiv  Detail & Related papers  (2024-01-02T07:40:12Z)
• Application of frozen large-scale models to multimodal task-oriented dialogue [0.0]
  We use the existing Large Language Models ENnhanced to See Framework (LENS Framework) to test the feasibility of multimodal task-oriented dialogues. The LENS Framework has been proposed as a method to solve computer vision tasks without additional training and with fixed parameters of pre-trained models.
  arXiv  Detail & Related papers  (2023-10-02T01:42:28Z)
• Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models [52.24756457516834]
  We propose a novel "Self-Explanation" prompting strategy to enhance the comprehension abilities of Large Language Models (LLMs) This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks. Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts.
  arXiv  Detail & Related papers  (2023-09-22T15:41:34Z)
• Multi-View Zero-Shot Open Intent Induction from Dialogues: Multi Domain Batch and Proxy Gradient Transfer [16.804434185847363]
  In Task Oriented Dialogue (TOD) system, detecting and inducing new intents are two main challenges to apply the system in the real world. We suggest the semantic multi-view model to resolve these two challenges. We introduce a novel method PGT, which employs the Siamese network to fine-tune the model with a clustering method directly.
  arXiv  Detail & Related papers  (2023-03-23T08:30:35Z)
• PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment [58.46761798403072]
  A model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across multiple domains. Despite significant progress, an ADEM that works well in one domain does not necessarily generalize to another. We propose a Panel of Experts (PoE) network that consists of a shared transformer encoder and a collection of lightweight adapters.
  arXiv  Detail & Related papers  (2022-12-18T02:26:50Z)
• 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)
• "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)
• High-Quality Diversification for Task-Oriented Dialogue Systems [18.455916009255485]
  Training DRL agents with diverse dialogue trajectories prepare them well for rare user requests and unseen situations. One effective diversification method is to let the agent interact with a diverse set of learned user models. We propose a novel dialogue diversification method for task-oriented dialogue systems trained in simulators.
  arXiv  Detail & Related papers  (2021-06-02T02:10:07Z)
• Meta Dialogue Policy Learning [58.045067703675095]
  We propose Deep Transferable Q-Network (DTQN) to utilize shareable low-level signals between domains. We decompose the state and action representation space into feature subspaces corresponding to these low-level components. In experiments, our model outperforms baseline models in terms of both success rate and dialogue efficiency.
  arXiv  Detail & Related papers  (2020-06-03T23:53:06Z)
• CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset [58.910961297314415]
  CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi.
  arXiv  Detail & Related papers  (2020-02-27T03:06:35Z)

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.