Knowledge Enhanced Opinion Generation from an Attitude

Zhe Ye, Ruihua Song, Hao Fu, Pingping Lin, Jian-Yun Nie, Fang Li

Abstract: Mining opinion is essential for consistency and persona of a chatbot. However, mining existing opinions suffers from data sparsity. Toward a given entity, we cannot always find a proper sentence that expresses desired sentiment. In this paper, we propose to generate opinion sentences for a given attitude, i.e., an entity and sentiment polarity pair. We extract attributes of a target entity from a knowledge base and specific keywords from its description. The attributes and keywords are integrated with knowledge graph embeddings, and fed into an encoder-decoder generation framework. We also propose an auxiliary task that predicts attributes using the generated sentences, aiming to avoid common opinions. Experimental results indicate that our approach significantly outperforms baselines in automatic and human evaluation.