Precise Description Generation for Knowledge Base Entities with Local Pointer Network

Verbalization of knowledge base (KB) facts about an entity allows users to absorb information from KB more easily. The drawback of most previous work is that they cannot generalize to unseen frames. This work introduces the task of precise KB verbalization that is aimed at generating an exact description for the given factual triples. We propose a novel sequence-to-sequence (seq2seq) model with the local pointer network to deal with this task. The approach to training data construction is also explored. Experimental results show our method improves the performances in terms of Meteor and slot error rates. Human evaluation is also performed to confirm the effectiveness of our model.