no code implementations • EMNLP 2021 • Chenhan Yuan, Hoda Eldardiry
We propose a VAE-based unsupervised relation extraction technique that overcomes this limitation by using the classifications as an intermediate variable instead of a latent variable.
2 code implementations • 10 Mar 2024 • Gang Hu, Ke Qin, Chenhan Yuan, Min Peng, Alejandro Lopez-Lira, Benyou Wang, Sophia Ananiadou, Wanlong Yu, Jimin Huang, Qianqian Xie
While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity.
2 code implementations • 20 Feb 2024 • Qianqian Xie, Weiguang Han, Zhengyu Chen, Ruoyu Xiang, Xiao Zhang, Yueru He, Mengxi Xiao, Dong Li, Yongfu Dai, Duanyu Feng, Yijing Xu, Haoqiang Kang, Ziyan Kuang, Chenhan Yuan, Kailai Yang, Zheheng Luo, Tianlin Zhang, Zhiwei Liu, Guojun Xiong, Zhiyang Deng, Yuechen Jiang, Zhiyuan Yao, Haohang Li, Yangyang Yu, Gang Hu, Jiajia Huang, Xiao-Yang Liu, Alejandro Lopez-Lira, Benyou Wang, Yanzhao Lai, Hao Wang, Min Peng, Sophia Ananiadou, Jimin Huang
This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs.
1 code implementation • 12 Feb 2024 • Xiao Zhang, Ruoyu Xiang, Chenhan Yuan, Duanyu Feng, Weiguang Han, Alejandro Lopez-Lira, Xiao-Yang Liu, Sophia Ananiadou, Min Peng, Jimin Huang, Qianqian Xie
We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks.
no code implementations • 7 Oct 2023 • Chenhan Yuan, Hoda Eldardiry
This helps address the lack of interpretability in existing TKGR models and provides a universal explanation approach applicable across various models.
1 code implementation • 2 Oct 2023 • Chenhan Yuan, Qianqian Xie, Jimin Huang, Sophia Ananiadou
In this paper, we introduce the first task of explainable temporal reasoning, to predict an event's occurrence at a future timestamp based on context which requires multiple reasoning over multiple events, and subsequently provide a clear explanation for their prediction.
no code implementations • 11 Apr 2023 • Chenhan Yuan, Qianqian Xie, Sophia Ananiadou
The current shortcomings of ChatGPT on temporal relation extraction are also discussed in this paper.
no code implementations • 26 Sep 2020 • Chenhan Yuan, Ryan Rossi, Andrew Katz, Hoda Eldardiry
To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an "Encoder-Decoder" architecture to perform self-supervised learning so the encoder can extract relation information.
no code implementations • 26 Sep 2020 • Chenhan Yuan, Ryan Rossi, Andrew Katz, Hoda Eldardiry
In this paper, we relax this strong assumption by a weaker distant supervision assumption to address the second issue and propose a novel sentence distribution estimator model to address the first problem.
no code implementations • CCNLG (ACL) 2019 • Chenhan Yuan, Yi-chin Huang, Cheng-Hung Tsai
The objective results showed that the proposed method is capable of generating texts with less time than others and the generated text is related to the user-defined topic and sentiment.
no code implementations • 20 Apr 2019 • Chenhan Yuan, Yi-chin Huang
The author-specific word usage is a vital feature to let readers perceive the writing style of the author.