DeepAI AI Chat
Log In Sign Up

Response Generation by Context-aware Prototype Editing

by   Yu Wu, et al.
Beihang University

Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses. We propose a new paradigm for response generation, that is response generation by editing, which significantly increases the diversity and informativeness of the generation results. Our assumption is that a plausible response can be generated by slightly revising an existing response prototype. The prototype is retrieved from a pre-defined index and provides a good start-point for generation because it is grammatical and informative. We design a response editing model, where an edit vector is formed by considering differences between a prototype context and a current context, and then the edit vector is fed to a decoder to revise the prototype response for the current context. Experiment results on a large scale dataset demonstrate that the response editing model outperforms generative and retrieval-based models on various aspects.


Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory

The ability of a dialog system to express prespecified language style du...

Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory

For dialogue response generation, traditional generative models generate...

Generating Sentences by Editing Prototypes

We propose a new generative model of sentences that first samples a prot...

Learning from Exemplars and Prototypes in Machine Learning and Psychology

This paper draws a parallel between similarity-based categorisation mode...

Augmenting Neural Response Generation with Context-Aware Topical Attention

Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success i...

Post-edit Analysis of Collective Biography Generation

Text generation is increasingly common but often requires manual post-ed...

N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models

Avoiding the generation of responses that contradict the preceding conte...