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Single Example Can Improve Zero-Shot Data Generation
We explore two approaches to the generation of task-oriented utterances: in the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training.
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding
Natural language understanding is an important task in modern dialogue systems.
Machine Translation Models Stand Strong in the Face of Adversarial Attacks
Through our investigation, we provide evidence that machine translation models display robustness displayed robustness against best performed known adversarial attacks, as the degree of perturbation in the output is directly proportional to the perturbation in the input.
Template-based Approach to Zero-shot Intent Recognition
We outperform previous state-of-the-art f1-measure by up to 16\% for unseen intents, using intent labels and user utterances and without accessing external sources (such as knowledge bases).
A Single Example Can Improve Zero-Shot Data Generation
In the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training.
A Differentiable Language Model Adversarial Attack on Text Classifiers
Moreover, due to the usage of the fine-tuned language model, the generated adversarial examples are hard to detect, thus current models are not robust.
