1. arXiv
    WaNLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
  2. NAACL
    Reframing Human-AI Collaboration for Generating Free-Text Explanations
    Wiegreffe, Sarah, Hessel, Jack, Swayamdipta, Swabha, Riedl, Mark,  and Choi, Yejin
    In Proc. of NAACL 2022
  3. NAACL
    Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection
    In Proc. of NAACL 2022
  4. ICML
    Understanding Dataset Difficulty with 𝒱-Usable Information
    In Proc. of ICML 2022


  1. NeurIPS
    MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
    Pillutla, KrishnaSwayamdipta, Swabha, Zellers, Rowan, Thickstun, John, Wellecks, Sean, Choi, Yejin,  and Harchaoui, Zaid
    In Proc. of NeurIPS 2021
  2. EMNLP
    Sister Help: Data Augmentation for Frame-Semantic Role Labeling
    Pancholy, Ayush, Petruck, Miriam R. L.,  and Swayamdipta, Swabha
    In Proc. of LAW-DMR Workshop at EMNLP 2021
  3. EMNLP
    Contrastive Explanations for Model Interpretability
    Jacovi, AlonSwayamdipta, Swabha, Ravfogel, Shauli, Elazar, Yanai, Choi, Yejin,  and Goldberg, Yoav
    In Proc. of EMNLP 2021
  4. ACL
    DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
    Liu, AlisaSap, Maarten, Lu, Ximing, Swayamdipta, Swabha, Bhagavatula, Chandra, Smith, Noah A.,  and Choi, Yejin
    In Proc. of ACL 2021
  5. EACL
    Challenges in Automated Debiasing for Toxic Language Detection
    In Proc. of EACL 2021


  1. EMNLP
    G-DAUG: Generative Data Augmentation for Commonsense Reasoning
    Yang, Yiben, Malaviya, Chaitanya, Fernandez, Jared, Swayamdipta, Swabha, LeBras, Ronan, Wang, Ji-Ping, Bhagavatula, Chandra,  Choi, Yejin and 1 more author
    In Proc. of EMNLP Jun 2020