1. W. Chen, J. Tian, L. Xiao, H. He*, and Y. Jin, “A semantically consistent and syntactically variational encoder-decoder framework for paraphrase generation,” in Proceedings of the 28th international conference on computational linguistics, 2020, pp. 1186–1198.

2. W. Chen, J. Tian, L. Xiao, H. He*, and Y. Jin, “Exploring logically dependent multi-task learning with causal inference.,” in Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), https://www.aclweb.org/anthology/2020.emnlp-main.173, 2020, pp. 2213–2225.

3. W. Chen, J. Tian, Y. Li, H. He*, and Y. Jin, “De-confounded variational encoder-decoder for logical table-to-text generation,” in Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, 2021, pp. 5532–5542.

4. J. Tian, Y. Li, W. Chen, L. Xiao, H. He*, and Y. Jin, “Diagnosing the first-order logical reasoning ability through LogicNLI,” in Proceedings of the 2021 conference on empirical methods in natural language processing, 2021, pp. 3738–3747.

5. C. Fan, W. Chen, J. Tian, Y. Li, H. He*, and Y. Jin, “Accurate use of label dependency in multi-label text classification through the lens of causality,” Applied Intelligence, vol. 53, no. 19, pp. 21841–21857, 2023.

6. C. Fan, W. Chen, J. Tian, Y. Li, H. He*, and Y. Jin, “Unlock the potential of counterfactually-augmented data in out-of-distribution generalization,” Expert Systems with Applications, vol. 238, p. 122066, 2024.

7. C. Fan, J. Chen, Y. Jin, and H. He*, “Can large language models serve as rational players in game theory? a systematic analysis,” in Proceedings of the AAAI conference on artificial intelligence, 2024, pp. 17960–17967.