Abstract
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.