| Adversarial NLI: A new benchmark for natural language understanding Y Nie, A Williams, E Dinan, M Bansal, J Weston, D Kiela arXiv preprint arXiv:1910.14599, 2019 | 469 | 2019 |
| Combining Fact Extraction and Verification with Neural Semantic Matching Networks Y Nie, H Chen, M Bansal AAAI 2019, arXiv preprint:1811.07039, 2018 | 240 | 2018 |
| Dynabench: Rethinking benchmarking in NLP D Kiela, M Bartolo, Y Nie, D Kaushik, A Geiger, Z Wu, B Vidgen, G Prasad, ... arXiv preprint arXiv:2104.14337, 2021 | 152 | 2021 |
| Shortcut-stacked sentence encoders for multi-domain inference Y Nie, M Bansal EMNLP 2017 RepEval Workshop, 2017 | 146 | 2017 |
| Revealing the importance of semantic retrieval for machine reading at scale Y Nie, S Wang, M Bansal arXiv preprint arXiv:1909.08041, 2019 | 114 | 2019 |
| Analyzing Compositionality-Sensitivity of NLI Models Y Nie, Y Wang, M Bansal AAAI 2019, arXiv preprint:1811.07033, 2018 | 72 | 2018 |
| What can we learn from collective human opinions on natural language inference data? Y Nie, X Zhou, M Bansal arXiv preprint arXiv:2010.03532, 2020 | 47 | 2020 |
| I like fish, especially dolphins: Addressing contradictions in dialogue modeling Y Nie, M Williamson, M Bansal, D Kiela, J Weston arXiv preprint arXiv:2012.13391, 2020 | 44 | 2020 |
| The curse of performance instability in analysis datasets: Consequences, source, and suggestions X Zhou, Y Nie, H Tan, M Bansal arXiv preprint arXiv:2004.13606, 2020 | 31 | 2020 |
| ConjNLI: Natural language inference over conjunctive sentences S Saha, Y Nie, M Bansal arXiv preprint arXiv:2010.10418, 2020 | 24 | 2020 |
| To what extent do human explanations of model behavior align with actual model behavior? G Prasad, Y Nie, M Bansal, R Jia, D Kiela, A Williams arXiv preprint arXiv:2012.13354, 2020 | 16 | 2020 |
| Simple compounded-label training for fact extraction and verification Y Nie, L Bauer, M Bansal Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER …, 2020 | 8 | 2020 |
| Distributed NLI: learning to predict human opinion distributions for language reasoning X Zhou, Y Nie, M Bansal arXiv preprint arXiv:2104.08676, 2021 | 7 | 2021 |
| Mlp architectures for vision-and-language modeling: An empirical study Y Nie, L Li, Z Gan, S Wang, C Zhu, M Zeng, Z Liu, M Bansal, L Wang arXiv preprint arXiv:2112.04453, 2021 | 5 | 2021 |
| TVLT: Textless Vision-Language Transformer Z Tang, J Cho, Y Nie, M Bansal arXiv preprint arXiv:2209.14156, 2022 | 4 | 2022 |
| Investigating transfer learning in multilingual pre-trained language models through Chinese natural language inference H Hu, H Zhou, Z Tian, Y Zhang, Y Ma, Y Li, Y Nie, K Richardson arXiv preprint arXiv:2106.03983, 2021 | 4 | 2021 |
| 3DGen: Triplane Latent Diffusion for Textured Mesh Generation A Gupta, W Xiong, Y Nie, I Jones, B Oğuz arXiv preprint arXiv:2303.05371, 2023 | | 2023 |
| CLIP-Layout: Style-Consistent Indoor Scene Synthesis with Semantic Furniture Embedding J Liu, W Xiong, I Jones, Y Nie, A Gupta, B Oğuz arXiv preprint arXiv:2303.03565, 2023 | | 2023 |
| Data-Driven Natural Language Inference Y Nie The University of North Carolina at Chapel Hill, 2022 | | 2022 |
| Supplementary Materials for TVLT: Textless Vision-Language Transformer Z Tang, J Cho, Y Nie, M Bansal | | |
Mohit BansalProfessor, Computer Science, UNC Chapel HillVerified email at cs.unc.edu
