Xinyang Li


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MGR: Multi-generator Based Rationalization
Wei Liu | Haozhao Wang | Jun Wang | Ruixuan Li | Xinyang Li | YuanKai Zhang | Yang Qiu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Rationalization is to employ a generator and a predictor to construct a self-explaining NLP model in which the generator selects a subset of human-intelligible pieces of the input text to the following predictor. However, rationalization suffers from two key challenges, i.e., spurious correlation and degeneration, where the predictor overfits the spurious or meaningless pieces solely selected by the not-yet well-trained generator and in turn deteriorates the generator. Although many studies have been proposed to address the two challenges, they are usually designed separately and do not take both of them into account. In this paper, we propose a simple yet effective method named MGR to simultaneously solve the two problems. The key idea of MGR is to employ multiple generators such that the occurrence stability of real pieces is improved and more meaningful pieces are delivered to the predictor. Empirically, we show that MGR improves the F1 score by up to 20.9% as compared to state-of-the-art methods.

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PersLEARN: Research Training through the Lens of Perspective Cultivation
Yu-Zhe Shi | Shiqian Li | Xinyi Niu | Qiao Xu | Jiawen Liu | Yifan Xu | Shiyu Gu | Bingru He | Xinyang Li | Xinyu Zhao | Zijian Zhao | Yidong Lyu | Zhen Li | Sijia Liu | Lin Qiu | Jinhao Ji | Lecheng Ruan | Yuxi Ma | Wenjuan Han | Yixin Zhu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

Scientific research is inherently shaped by its authors’ perspectives, influenced by various factorssuch as their personality, community, or society. Junior researchers often face challenges in identifying the perspectives reflected in the existing literature and struggle to develop their own viewpoints. In response to this issue, we introduce PersLEARN , a tool designed to facilitate the cultivation of scientific perspectives, starting from a basic seed idea and progressing to a well-articulated framework. By interacting with a prompt-based model, researchers can develop their perspectives explicitly. Our humanstudy reveals that scientific perspectives developed by students using PersLEARN exhibit a superior level of logical coherence and depth compared to those that did not. Furthermore, our pipeline outperforms baseline approaches across multiple domains of literature from various perspectives. These results suggest that PersLEARN could help foster a greater appreciation of diversity in scientific perspectives as an essential component of research training.