Adversarial learning of neural user simulators for dialogue policy optimisation

• URL: http://arxiv.org/abs/2306.00858v1
• Date: Thu, 1 Jun 2023 16:17:16 GMT
• Title: Adversarial learning of neural user simulators for dialogue policy optimisation
• Authors: Simon Keizer, Caroline Dockes, Norbert Braunschweiler, Svetlana Stoyanchev, Rama Doddipatla
• Abstract summary: Reinforcement learning based dialogue policies are typically trained in interaction with a user simulator. Current data-driven simulators are trained to accurately model the user behaviour in a dialogue corpus. We propose an alternative method using adversarial learning, with the aim to simulate realistic user behaviour with more variation.
• Score: 14.257597015289512
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reinforcement learning based dialogue policies are typically trained in interaction with a user simulator. To obtain an effective and robust policy, this simulator should generate user behaviour that is both realistic and varied. Current data-driven simulators are trained to accurately model the user behaviour in a dialogue corpus. We propose an alternative method using adversarial learning, with the aim to simulate realistic user behaviour with more variation. We train and evaluate several simulators on a corpus of restaurant search dialogues, and then use them to train dialogue system policies. In policy cross-evaluation experiments we demonstrate that an adversarially trained simulator produces policies with 8.3% higher success rate than those trained with a maximum likelihood simulator. Subjective results from a crowd-sourced dialogue system user evaluation confirm the effectiveness of adversarially training user simulators.

Related papers

• Reliable LLM-based User Simulator for Task-Oriented Dialogue Systems [2.788542465279969]
  This paper introduces DAUS, a Domain-Aware User Simulator. We fine-tune DAUS on real examples of task-oriented dialogues. Results on two relevant benchmarks showcase significant improvements in terms of user goal fulfillment.
  arXiv  Detail & Related papers  (2024-02-20T20:57:47Z)
• Robust Visual Sim-to-Real Transfer for Robotic Manipulation [79.66851068682779]
  Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained policies often fail when transferred to real robots. One common approach to bridge the visual sim-to-real domain gap is domain randomization (DR)
  arXiv  Detail & Related papers  (2023-07-28T05:47:24Z)
• Improving Conversational Recommendation Systems via Counterfactual Data Simulation [73.4526400381668]
  Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. We propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs.
  arXiv  Detail & Related papers  (2023-06-05T12:48:56Z)
• In-Context Learning User Simulators for Task-Oriented Dialog Systems [1.7086737326992172]
  This paper presents a novel application of large language models in user simulation for task-oriented dialog systems. By harnessing the power of these models, the proposed approach generates diverse utterances based on user goals and limited dialog examples.
  arXiv  Detail & Related papers  (2023-06-01T15:06:11Z)
• Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator [37.590563896382456]
  We propose an interactive evaluation framework for Task-Oriented Dialogue (TOD) systems. We first build a goal-oriented user simulator based on pre-trained models and then use the user simulator to interact with the dialogue system to generate dialogues. Experimental results show that RL-based TOD systems trained by our proposed user simulator can achieve nearly 98% inform and success rates.
  arXiv  Detail & Related papers  (2022-10-26T07:41:32Z)
• Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems [80.77917437785773]
  Task-oriented dialogue systems ( TDSs) are assessed mainly in an offline setting or through human evaluation. We propose a metaphorical user simulator for end-to-end TDS evaluation, where we define a simulator to be metaphorical if it simulates user's analogical thinking in interactions with systems. We also propose a tester-based evaluation framework to generate variants, i.e., dialogue systems with different capabilities.
  arXiv  Detail & Related papers  (2022-04-02T05:11:03Z)
• Learning Interactive Driving Policies via Data-driven Simulation [125.97811179463542]
  Data-driven simulators promise high data-efficiency for driving policy learning. Small underlying datasets often lack interesting and challenging edge cases for learning interactive driving. We propose a simulation method that uses in-painted ado vehicles for learning robust driving policies.
  arXiv  Detail & Related papers  (2021-11-23T20:14:02Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)
• Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition [64.06167416127386]
  We propose Multi-Agent Dialog Policy Learning, which regards both the system and the user as the dialog agents. Two agents interact with each other and are jointly learned simultaneously. Results show that our method can successfully build a system policy and a user policy simultaneously.
  arXiv  Detail & Related papers  (2020-04-08T04:51:40Z)

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.