ColdGANs: Taming Language GANs with Cautious Sampling Strategies
Table 2: Results on Question Generation (QG) and Abstractive Summarization (Summ.) tasks.
['[EMPTY]', '#params', 'QG (SQuAD) BLEU-1', 'QG (SQuAD) BLEU-4', 'Summ. (CNN/DM) ROUGE-1', 'Summ. (CNN/DM) ROUGE-L', 'Summ. (CNN/DM) BLEU-4']
For both tasks, we used data and evaluation metrics released by dong2019unified. The best performance is achieved with the experiment emphasizing the ColdGANnucleus exploration the most, with ϵ=.9 and T=.2. Over 10 independent training runs , we also observed very stable results for this model, with a standard deviation of the average BLEU-4 lower than .09 on the test set.