Conversation Generation with Concept Flow

Houyu Zhang, Zhenghao Liu, Chenyan Xiong, Zhiyuan Liu

Keywords: Grounded Natural Language Generation, Conversation Response Generation, ConceptNet, ConceptFlow, Conversation Flow

TL;DR: This paper presents ConceptFlow that explicitly models the conversation flow in commonsense knowledge graph for better conversation generation.

Abstract: Human conversations naturally evolve around related entities and connected concepts, while may also shift from topic to topic. This paper presents ConceptFlow, which leverages commonsense knowledge graphs to explicitly model such conversation flows for better conversation response generation. ConceptFlow grounds the conversation inputs to the latent concept space and represents the potential conversation flow as a concept flow along the commonsense relations. The concept is guided by a graph attention mechanism that models the possibility of the conversation evolving towards different concepts. The conversation response is then decoded using the encodings of both utterance texts and concept flows, integrating the learned conversation structure in the concept space. Our experiments on Reddit conversations demonstrate the advantage of ConceptFlow over previous commonsense aware dialog models and fine-tuned GPT-2 models, while using much fewer parameters but with explicit modeling of conversation structures.

Code: https://drive.google.com/open?id=1JCO2lsFDbUMsEc3kloR1FSzxxuDXJ6CF

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1911.02707/code)