Discourse-Aware Neural Rewards for Coherent Text Generation
Table 1: Evaluation results for generated sequences by models and baselines. We bold the top performing result. The second to fourth columns list word-level scores. Columns AB1, AB4, and AR-L list action-level scores (§6.1). Columns SCB1, SCB4, and SCR-L list state change level scores (§6.1).
['Model', 'BLEU-1', 'BLEU-4', 'R-L', 'AB1', 'AB4', 'AR-L', 'SCB1', 'SCB4', 'SCR-L']
As baselines, we report results for a model trained only with cross-entropy loss (MLE) and for re-implemented versions of models from Rennie et al. These baselines achieved state of the art results in image captioning and document summarization tasks. We found, however, that their high γ (1 and 0.9984, respectively) led to low fluency, resulting in reduced performance on word-level scores.