Abstract: Embodiments described herein provide adversarial attacks targeting the cross-lingual generalization ability of massive multilingual representations, demonstrating their effectiveness on multilingual models for natural language inference and question answering. An efficient adversarial training scheme can thus be implemented with the adversarial attacks, which takes the same number of steps as standard supervised training and show that it encourages language-invariance in representations, thereby improving both clean and robust accuracy.

Abstract: Embodiments provide a software framework for evaluating and troubleshooting real-world task-oriented bot systems. Specifically, the evaluation framework includes a generator that infers dialog acts and entities from bot definitions and generates test cases for the system via model-based paraphrasing. The framework may also include a simulator for task-oriented dialog user simulation that supports both regression testing and end-to-end evaluation. The framework may also include a remediator to analyze and visualize the simulation results, remedy some of the identified issues, and provide actionable suggestions for improving the task-oriented dialog system.

Abstract: Embodiments described herein provide adversarial attacks targeting the cross-lingual generalization ability of massive multilingual representations, demonstrating their effectiveness on multilingual models for natural language inference and question answering. An efficient adversarial training scheme can thus be implemented with the adversarial attacks, which takes the same number of steps as standard supervised training and show that it encourages language-invariance in representations, thereby improving both clean and robust accuracy.

Abstract: Embodiments described herein provide systems and methods for generating an adversarial sample with inflectional perturbations for training a natural language processing (NLP) system. A natural language sentence is received at an inflection perturbation module. Tokens are generated from the natural language sentence. For each token that has a part of speech that is a verb, adjective, or an adverb, an inflected form is determined. An adversarial sample of the natural language sentence is generated by detokenizing inflected forms of the tokens. The NLP system is trained using the adversarial sample.

Abstract: Embodiments described herein provide systems and methods for generating an adversarial sample with inflectional perturbations for training a natural language processing (NLP) system. A natural language sentence is received at an inflection perturbation module. Tokens are generated from the natural language sentence. For each token that has a part of speech that is a verb, adjective, or an adverb, an inflected form is determined. An adversarial sample of the natural language sentence is generated by detokenizing inflected forms of the tokens. The NLP system is trained using the adversarial sample.