To address this issue, we adopt a denoising training strategy inspired by unsupervised machine translation (Lample et al., 2018(Lample et al., , 2019, and obtain a DST model that is more robust to the attribute transformation.
we apply a noise function g(x) to the utterances, and modify the DST model training loss as: where the noise function corrupts the input utterance by performing word drops and word order shuffling as specified in Lample et al.