Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning
Table 3: Performance of length control RL in “test-4k” (ML results also included for comparison). Obviously highest scores (0.4 larger than the second best) are in bolded font, the scores in italic font are significantly worse score (2 lower than best socre).
['model', 'parameter', '25 R-1', '25 R-2', '25 R-L', '45 R-1', '45 R-2', '45 R-L', '65 R-1', '65 R-2', '65 R-L', 'svar(±std)']
We evaluate our models with sentence length of 25, 45 and 65, which represent short, median and long sentences separately. Results may vary after each training process since RL is usually unstable, so we repeat training for multiple times in each model and statistic the results on average. We make experiments of MTS-RL on three LC models, for WLI models LenLInit and LenInit, accuracy and svar both rise under the selected dth increasing, which means hyper-parameter dth in MTS-RL can be used to adjust the LC capacity. However, for the RLI model LenMC, results show there is no obvious distinction in scores when we use different dth. Hence, we adopt SCD-RL training algorithm for LenMC, the results show our SCD-RL algorithm can control LC capacity for RLI model as MTS-RL does for the WLI model. and SCD-RL can also manage the LC capacity for WLI models. Overall, two RL training algorithms prevent the model from length control collapsing, and make this capacity controllable via their own hyper-parameters.