Self-Regulated Interactive Sequence-to-Sequence Learning
Table 4: Evaluation of models at early stopping points on the Books test set (beam search with width five).
['[BOLD] Model', '[BOLD] Books test  [BOLD] BLEU↑', '[BOLD] Books test  [BOLD] TER↓', '[BOLD] Books test  [BOLD] Cost↓']
The baseline was trained on WMT parallel data without any regulation. The regulator was trained on IWSLT and evaluated on Books, the Seq2Seq model is further trained for one epoch on Books. The costs are measured in character edits and clicks. The best result in terms of BLEU and TER is achieved by the Reg2 model, even outperforming the model with full feedback. As observed for the IWSLT domain (cf. , self-training is very effective, but is outperformed by the Reg2 model and roughly on par with the Reg3 model.