Response Generation by Context-aware Prototype Editing (1806.07042v4)

Abstract: 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.

• The paper introduces a paradigm that edits pre-existing response prototypes to generate contextually appropriate outputs.
• It employs a context-aware edit vector that adjusts prototype responses, improving diversity and informativeness.
• Experimental results demonstrate that the hybrid retrieval-editing model outperforms traditional generative methods.

The paper "Response Generation by Context-aware Prototype Editing" introduces a novel approach for open-domain response generation, addressing the common issue of producing short and uninformative responses that often plague generative models. The authors propose a response generation paradigm based on editing pre-existing prototype responses, which leads to increased diversity and informativeness.

Key Concepts and Methodology

The core idea is that plausible and contextually appropriate responses can be generated by making slight revisions to pre-existing responses, referred to as prototypes. These prototypes are fetched from a predefined index and serve as high-quality starting points due to their grammatical correctness and informativeness.

The methodology involves a few crucial steps:

1. Prototype Retrieval: Given a new conversational context, a relevant response prototype is retrieved from the index, ensuring that the starting point for generation is contextually appropriate.
2. Context-Aware Editing: An edit vector is calculated by considering the differences between the retrieved prototype context and the current conversational context. This vector captures the necessary adjustments required to make the prototype suitable for the current context.
3. Response Generation: The edit vector is input into a decoder that revises the prototype response, effectively tailoring it to fit the new context.

Experimental Results

The proposed response editing model was evaluated on a large-scale dataset, showing superior performance compared to both purely generative models and retrieval-based models. The evaluation metrics highlighted improvements in response diversity and informativeness, suggesting that the context-aware prototype editing approach offers substantial benefits over traditional methods.

Conclusion

By harnessing existing responses and refining them with context-aware edits, this approach mitigates common issues of short and non-informative outputs in open-domain response generation. The paper provides a promising direction for future research, emphasizing the potential of hybrid models that integrate both retrieval and generative techniques to enhance conversational AI systems.