Zero-Shot Dialog Generation with Cross-Domain Latent Actions

2018/05/13 by Tiancheng Zhao, Zhao, Tiancheng, Maxine Eskénazi +1 · 3 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1805.04803

openalex publication_date 2018/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper introduces zero-shot dialog generation (ZSDG), as a step towards neural dialog systems that can instantly generalize to new situations with minimal data. ZSDG enables an end-to-end generative dialog system to generalize to a new domain for which only a domain description is provided and no training dialogs are available. Then a novel learning framework, Action Matching, is proposed. This algorithm can learn a cross-domain embedding space that models the semantics of dialog responses which, in turn, lets a neural dialog generation model generalize to new domains. We evaluate our methods on a new synthetic dialog dataset, and an existing human-human dialog dataset. Results show that our method has superior performance in learning dialog models that rapidly adapt their behavior to new domains and suggests promising future research.

Citations

• Adam: A Method for Stochastic Optimization
• Recurrent Neural Network Regularization
• Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems
• A Network-based End-to-End Trainable Task-oriented Dialogue System
• Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
• Pointer Sentinel Mixture Models
• One-Shot Imitation Learning
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Cited by

• Resource Constrained Dialog Policy Learning via Differentiable Inductive Logic Programming
• When is it permissible for artificial intelligence to lie? A trust-based\n approach
• Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges

Related

• Domain Adaptive Dialog Generation via Meta Learning
• Few-shot Natural Language Generation for Task-Oriented Dialog
• Schema-Guided Paradigm for Zero-Shot Dialog
• LAD: Language Models as Data for Zero-Shot Dialog
• Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level Generation