Differentiable Scheduled Sampling for Credit Assignment
Kartik Goyal, Chris Dyer, Taylor Berg-Kirkpatrick · 2017
We demonstrate that a continuous relaxation of the argmax operation can be used to create a differentiable approximation to greedy decoding for sequence-tosequence (seq2seq) models.By incorporating this approximation into the scheduled sampling training procedure (Bengio et al., 2015)-a well-known technique for correcting exposure bias-we introduce a new training objective that is continuous and differentiable everywhere and that can provide informative gradients near points where previous decoding decisions change their value.In addition, by using a related approximation, we demonstrate a similar approach to sampled-based training.Finally, we show that our approach outperforms cross-entropy training and scheduled sampling procedures in two sequence prediction tasks: named entity recognition and machine translation.