Cue-word Driven Neural Response Generation with a Shrinking Vocabulary

Abstract

Open-domain response generation is the task of generating sensible and informative re-sponses to the source sentence. However, neural models tend to generate safe and mean-ingless responses. While cue-word introducing approaches encourage responses with concrete semantics and have shown tremendous potential, they still fail to explore di-verse responses during decoding. In this paper, we propose a novel but natural approach that can produce multiple cue-words during decoding, and then uses the produced cue-words to drive decoding and shrinks the decoding vocabulary. Thus the neural genera-tion model can explore the full space of responses and discover informative ones with efficiency. Experimental results show that our approach significantly outperforms several strong baseline models with much lower decoding complexity. Especially, our approach can converge to concrete semantics more efficiently during decoding.

References in corpus (21)

• Sequence to Sequence Learning with Neural Networks
• Decoupled Weight Decay Regularization
• BERTScore: Evaluating Text Generation with BERT
• A Deep Reinforced Model for Abstractive Summarization
• The Curious Case of Neural Text Degeneration
• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
• Topic Aware Neural Response Generation
• Towards a Human-like Open-Domain Chatbot
• A Simple, Fast Diverse Decoding Algorithm for Neural Generation
• Neural Responding Machine for Short-Text Conversation
• Insertion Transformer: Flexible Sequence Generation via Insertion Operations
• PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation
• DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
• KERMIT: Generative Insertion-Based Modeling for Sequences
• Non-Monotonic Sequential Text Generation
• Learning to Decode for Future Success
• Neural Response Generation with Dynamic Vocabularies
• Vocabulary Selection Strategies for Neural Machine Translation
• Sequence Modeling with Unconstrained Generation Order
• Steering Output Style and Topic in Neural Response Generation
• A Discrete CVAE for Response Generation on Short-Text Conversation