• S. Appalaraju, P. Tang, Q. Dong, N. Sankaran, Y. Zhou, and R. Manmatha, DocFormerv2: Local Features for Document Understanding, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 2, pp. 709–718, 2024. doi:10.1609/aaai.v38i2.27828
  • Y. Zhou and V. Srikumar, METAPROBE: A Representation- and Task-Agnostic Probe, In Proc. Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, 2023, p. 233–249. doi:10.18653/v1/2023.blackboxnlp-1.18
  • Y. Zhou and V. Srikumar, A Closer Look at How Fine-tuning Changes BERT, In Proc. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 1046–1061. doi:10.18653/v1/2022.acl-long.75
  • Y. Zhou and V. Srikumar, DirectProbe: Studying Representations without Classifiers, In Proc. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 5070–5083. doi:10.18653/v1/2021.naacl-main.401
  • T. Karidi, Y. Zhou, N. Schneider, O. Abend, and V. Srikumar, Putting Words in BERT’s Mouth: Navigating Contextualized Vector Spaces with Pseudowords, In Proc. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021, p. 10300–10313. doi:10.18653/v1/2021.emnlp-main.806
  • Y. Zhou, O. Koshorek, V. Srikumar, and J. Berant, A Simple Global Neural Discourse Parser, 2020. doi:10.48550/arXiv.2009.01312
  • O. Koshorek, G. Stanovsky, Y. Zhou, V. Srikumar, and J. Berant, On the Limits of Learning to Actively Learn Semantic Representations, In Proc. Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), 2019, p. 452–462. doi:10.18653/v1/K19-1042
  • Y. Zhou and V. Srikumar, Beyond Context: A New Perspective for Word Embeddings, In Proc. Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), 2019, p. 22–32. doi:10.18653/v1/S19-1003
  • Y. Zhou, S. Huang, X. Dai, and J. Chen, Resolving Coordinate Structures for Chinese Constituent Parsing, in Natural Language Processing and Chinese Computing, J. Li, H. Ji, D. Zhao, and Y. Feng, Eds. Cham: Springer International Publishing, 2015, vol. 9362, pp. 353–361.[Online]. Available: https://link.springer.com/10.1007/978-3-319-25207-0_30 [Accessed: Oct. 20, 2024].
  • TopoBERT: Exploring the topology of fine-tuned word representations - Archit Rathore, Yichu Zhou, Vivek Srikumar, Bei Wang, 2023, [Online]. Available: https://journals.sagepub.com/doi/abs/10.1177/14738716231168671 [Accessed: Oct. 20, 2024].
  • Discovering and Applying Geometric Properties for Probing Contextualized Representations - ProQuest, [Online]. Available: https://www.proquest.com/openview/ced2fcec12189e5c8e9839fce3bdbc3c/1?pq-origsite=gscholar&cbl=18750&diss=y [Accessed: Oct. 20, 2024].
  • Building a Chinese Dependency GraphBank | IEEE Conference Publication | IEEE Xplore, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7814465 [Accessed: Oct. 20, 2024].