Improving Robustness and Generality of NLP Models Using Disentangled Representations

Jiawei Wu
Jiawei Wu
Xiaoya Li
Xiaoya Li
Xiang Ao
Xiang Ao
Cited by: 0|Views21

Abstract:

Supervised neural networks, which first map an input $x$ to a single representation $z$, and then map $z$ to the output label $y$, have achieved remarkable success in a wide range of natural language processing (NLP) tasks. Despite their success, neural models lack for both robustness and generality: small perturbations to inputs can re...More

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