106 citations · 289 across the 13 of their papers we have counts for
7 papers · 1 filter
Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP
Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky +8
What are the units of text that we want to model? From bytes to multi-word expressions, text can be analyzed and generated at many granularities. Until recently, most natural langu…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
TraVLR: Now You See It, Now You Don't! A Bimodal Dataset for Evaluating Visio-Linguistic Reasoning
Keng Ji Chow, Samson Tan, Min-Yen Kan
Numerous visio-linguistic (V+L) representation learning methods have been developed, yet existing datasets do not adequately evaluate the extent to which they represent visual and…
Interpreting the Robustness of Neural NLP Models to Textual Perturbations
Yunxiang Zhang, Liangming Pan, Samson Tan +1
Modern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However,…
Reliability Testing for Natural Language Processing Systems
Samson Tan, Shafiq Joty, Kathy Baxter +3
Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems…
Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots
Samson Tan, Shafiq Joty
Multilingual models have demonstrated impressive cross-lingual transfer performance. However, test sets like XNLI are monolingual at the example level. In multilingual communities,…
Robustness Gym: Unifying the NLP Evaluation Landscape
Karan Goel, Nazneen Rajani, Jesse Vig +6
Despite impressive performance on standard benchmarks, deep neural networks are often brittle when deployed in real-world systems. Consequently, recent research has focused on test…