Evaluating the Robustness of Neural Language Models to Input Perturbations
Milad Moradi, Matthias Samwald
2021Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing65 citationsDOIOpen Access PDF
Abstract
High-performance neural language models have obtained state-of-the-art results on a wide range of Natural Language Processing (NLP) tasks. However, results for common benchmark datasets often do not reflect model reliability and robustness when applied to noisy, real-world data. In this study, we design and implement various types of character-level and wordlevel perturbation methods to simulate realistic scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained.
Topics & Concepts
Robustness (evolution)Computer scienceLanguage modelArtificial intelligenceArtificial neural networkNatural language processingMachine learningChemistryGeneBiochemistryTopic ModelingNatural Language Processing TechniquesSoftware Engineering Research