Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning
Table 2: Results of ML training on standard “test-1951”
['model name', 'R-1', 'R-2', 'R-L', 'svar']
Although the evaluation score is not the unique objective in this research, it is of interest that how exactly the score is deprived by LC capacity. After individually comparing two WLI models and two RLI models, we find the two proposed models, LenLInit and LenMC, slightly corrupt LC capacity while improve the scores obviously.