no code implementations • 21 Jul 2021 • Zhongyang Li, Xiao Ding, Ting Liu, J. Edward Hu, Benjamin Van Durme
We present a conditional text generation framework that posits sentential expressions of possible causes and effects.
no code implementations • 1 Jul 2020 • Ryan Culkin, J. Edward Hu, Elias Stengel-Eskin, Guanghui Qin, Benjamin Van Durme
We introduce a novel paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrasing and discriminative span alignment.
1 code implementation • ICML 2020 • Greg Yang, Tony Duan, J. Edward Hu, Hadi Salman, Ilya Razenshteyn, Jerry Li
Randomized smoothing is the current state-of-the-art defense with provable robustness against $\ell_2$ adversarial attacks.
no code implementations • CONLL 2019 • J. Edward Hu, Abhinav Singh, Nils Holzenberger, Matt Post, Benjamin Van Durme
Producing diverse paraphrases of a sentence is a challenging task.
1 code implementation • NAACL 2019 • J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting.
no code implementations • 11 Jan 2019 • J. Edward Hu, Rachel Rudinger, Matt Post, Benjamin Van Durme
We present ParaBank, a large-scale English paraphrase dataset that surpasses prior work in both quantity and quality.
no code implementations • EMNLP (ACL) 2018 • Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning.