Improved Natural Language Generation via Loss Truncation
Table 2: HUSE, HUSE-D, and HUSE-Q scores for loss truncation and baselines. As shown, loss truncation outperforms all baselines on HUSE score.
['[EMPTY]', 'Loss trunc.', 'Trunc+reject ( [ITALIC] α=0.1)', 'Full samp.', 'Beam', 'top- [ITALIC] k ( [ITALIC] k=100)', 'top- [ITALIC] p ( [ITALIC] p=0.9)', 'GAN']
Using the HUSE score to measure the TV distance, we assessed whether loss truncation successfully improved our model in terms of distinguishability compared to log loss. is a better language model than the log loss model as measured by distinguishability.