Automated Chess Commentator Powered by Neural Chess Engine
Table 1: Automatic evaluation results.
['[BOLD] BLEU-4 (%)', '[BOLD] Temp', '[BOLD] Re', '[BOLD] KWG', '[BOLD] GAC', '[BOLD] SCC-weak', '[BOLD] SCC-strong', '[BOLD] SCC-mult']
Our SCC models outperform all of the baselines and previous state-of-the-art models. Temp is limited by the variety of templates. It is competitive with the neural models on Description and Quality due to limited expressions in these tasks. But when coming to Comparison, Planning and Contexts, Temp shows really bad performances. Re keeps flexibility by copying the sentences from training set. But it does not perform well, either. KWG and GAC provide competitive results. With the help of external information from powerful chess engines, GAC shows good performances on Quality and Comparison. Although our internal chess engine is no match for the external engines that GAC uses at playing chess, it turns out that our models with directly internal information can better bridge the semantic spaces of chess game and comment language. As for the comparisons within our models, SCC-strong turns to be better than SCC-weak, which supports our assumption that better skills enable more precise predictions, resulting in better comments. Training with multi-task learning seems to hurt the overall performances a little. But SCC-mult still has the state-of-the-art performances. And more important, it can react to all sub-tasks as a whole.