  In this paper, we explore a new approach for automated chess commentary
generation, which aims to generate chess commentary texts in different
categories (e.g., description, comparison, planning, etc.). We introduce a
neural chess engine into text generation models to help with encoding boards,
predicting moves, and analyzing situations. By jointly training the neural
chess engine and the generation models for different categories, the models
become more effective. We conduct experiments on 5 categories in a benchmark
Chess Commentary dataset and achieve inspiring results in both automatic and
human evaluations.
