Language generation system for sightseeing guidance using neural language model

Abstract

In sightseeing information navigation systems, the information presented by natural language has a potential to improve usability. Several systems tried to embed the informing contents in a prepared template for generating sentences which are useful for tourists, which is called a slot filling based method. However, it is difficult for the systems to generate diverse expressions and unseen patterns. To solve this problem, we propose a neural network based sentence generation method instead of using a slot filling based method. In this research, we construct the contents as a one-hot vector representation and construct the neural network based language generator and the one-hot content vectors for generating natural and understandable sentences. We collected a tourist information corpus via crowdsourcing. Existing language generation systems used word classes. However, these systems often connect words unnaturally. In this research, we also proposed a re-ranking system based on a neural language model to solve the problem. In our experiments, we confirmed the naturalness and validity of the sightseeing guidance sentences generated by our proposed method.