Researcher Portfolio
Jin, Zhijing
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society
Researcher Profile
Position: Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society
Additional IDs: ORCID:
https://orcid.org/0000-0003-0238-9024
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons283647
Publications
: Jin, Z., Chen, Y., Leeb, F., Gresele, L., Kamal, O., Lyu, Z., Blin, K., Gonzalez, F., Kleiman-Weiner, M., Sachan, M., & Schölkopf, B. (2023). CLadder: Assessing Causal Reasoning in Language Models. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds. ), Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper_files/paper/2023/hash/631bb9434d718ea309af82566347d607-Abstract-Conference.html. [PubMan] : Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., Ulmer, D., Schottmann, F., Batsuren, K., Sun, K., Sinha, K., Khalatbari, L., Ryskina, M., Frieske, R., Cotterell, R., & Jin, Z. (2023). A taxonomy and review of generalization research in NLP. Nature Machine Intelligence, 5, 1161-1174. doi:10.1038/s42256-023-00729-y. [PubMan] : Jin, Z., Levine, S., Gonzalez, F., Kamal, O., Sap, M., Sachan, M., Mihalcea, R., Tenenbaum, J., & Schölkopf, B. (2022). When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds. ), Advances in Neural Information Processing Systems 35 (pp. 28458-28473). Red Hook, NY: Curran Associates, Inc. [PubMan] : Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., & Schölkopf, B. (2022). Logical Fallacy Detection. In Findings of the Association for Computational Linguistics: EMNLP 2022 (pp. 7209-7227). Association for Computational Linguistics. doi:10.18653/v1/2022.findings-emnlp.532. [PubMan] : Ni, J., Jin, Z., Freitag, M., Sachan, M., & Schölkopf, B. (2022). Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance. In M. Carpuat, M.-C. de Marneffe, & I. V. M. Ruiz (Eds. ), Proceedings of the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 5303-5320). Stroudsburg, PA: Association for Computational Linguistics. [PubMan] : Keidar, D., Opedal, A., Jin, Z., & Sachan, M. (2022). Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang. In S. Muresan, P. Nakov, & A. Villavicencio (Eds. ), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (pp. 1422-1442). Stroudsburg, PA: Association for Computational Linguistics. [PubMan] : Jin, D., Jin, Z., Hu, Z., Vechtomova, O., & Mihalcea, R. (2022). Deep Learning for Text Style Transfer: A Survey. Computational linguistics, 48(1), 155-205. doi:10.1162/coli_a_00426. [PubMan] : Mattern, J., Jin, Z., Weggenmann, B., Schölkopf, B., & Sachan, M. (2022). Differentially Private Language Models for Secure Data Sharing. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds. ), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 4860-4873). Stroudsburg, PA: Association for Computational Linguistics. doi:10.18653/v1/2022.emnlp-main.323. [PubMan] : Jin, Z., von Kügelgen, J., Ni, J., Vaidhya, T., Kaushal, A., Sachan, M., & Schölkopf, B. (2021). Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP. In M.-F. Moens, X. Huang, L. Specia, & S.-W.-t. Yih (Eds. ), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) (pp. 9499-9513). Stroudsburg, PA: Association for Computational Linguistics. doi:10.18653/v1/2021.emnlp-main.748. [PubMan] : Jin, Z., Peng, Z., Vaidhya, T., Schölkopf, B., & Mihalcea, R. (2021). Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States. In M.-F. Moens, X. Huang, L. Specia, & S.-W.-t. Yih (Eds. ), Findings of the Association for Computational Linguistics: EMNLP 2021 (pp. 288-301). Stroudsburg, PA: Association for Computational Linguistics. doi:10.18653/v1/2021.findings-emnlp.27. [PubMan] : Jin, Z., Chauhan, G., Tse, B., Sachan, M., & Mihalcea, R. (2021). How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds. ), Findings of ACL: ACL-IJCNLP 2021 (pp. 3099-3113). Stroudsburg, Pennsylvania: Association for Computational Linguistics. doi:10.18653/v1/2021.findings-acl.273. [PubMan] : Guo, Q., Jin, Z., Wang, Z., Qiu, X., Zhang, W., Zhu, J., Zhang, Z., & Wipf, D. (2021). Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings. In A. Banerjee, & K. Fukumizu (Eds. ), Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021 (pp. 1828-1836). PMLR. Retrieved from http://proceedings.mlr.press/v130/guo21b.html. [PubMan] : Xing, X., Jin, Z., Jin, D., Wang, B., Zhang, Q., & Huang, X. (2020). Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis. In B. Webber, T. Cohn, Y. He, & Y. Liu (Eds. ), 2020 Conference on Empirical Methods in Natural Language Processing - Proceedings of the Conference (pp. 3594-3605). Stroudsburg, PA: Association for Computational Linguistics. doi:10.18653/v1/2020.emnlp-main.292. [PubMan]