Building a Conversational Agent Overnight with Dialogue Self-Play

Abstract

We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with just a task schema and an API client from the dialogue system developer, but it is also customizable to cater to task-specific interactions. Compared to the Wizard-of-Oz approach for data collection, M2M achieves greater diversity and coverage of salient dialogue flows while maintaining the naturalness of individual utterances. In the first phase, a simulated user bot and a domain-agnostic system bot converse to exhaustively generate dialogue "outlines", i.e. sequences of template utterances and their semantic parses. In the second phase, crowd workers provide contextual rewrites of the dialogues to make the utterances more natural while preserving their meaning. The entire process can finish within a few hours. We propose a new corpus of 3,000 dialogues spanning 2 domains collected with M2M, and present comparisons with popular dialogue datasets on the quality and diversity of the surface forms and dialogue flows.

11 pages, 4 figures

References in corpus (3)

• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
• Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
• End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning

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• Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation
• BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer
• A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset
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• Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation
• Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems
• Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
• Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI
• SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services
• Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring
• Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems
• Zero-Shot Dialogue State Tracking via Cross-Task Transfer
• SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching
• Generating Challenge Datasets for Task-Oriented Conversational Agents through Self-Play
• Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridging
• Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?
• Data-Efficient Methods for Dialogue Systems
• NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation
• Dialogue State Tracking with a Language Model using Schema-Driven Prompting