no code implementations • EMNLP 2021 • Wei Zhu, Xiaoling Wang, Yuan Ni, Guotong Xie
From this observation, we use mutual learning to improve BERT’s early exiting performances, that is, we ask each exit of a multi-exit BERT to distill knowledge from each other.
no code implementations • EMNLP (sdp) 2020 • Ling Chai, Guizhen Fu, Yuan Ni
We focus on systems for TASK1 (TASK 1A and TASK 1B) of CL-SciSumm Shared Task 2020 in this paper.
no code implementations • NAACL (BioNLP) 2021 • Wei Zhu, Yilong He, Ling Chai, Yunxiao Fan, Yuan Ni, Guotong Xie, Xiaoling Wang
First a RoBERTa model is first applied to give a local ranking of the candidate sentences.
no code implementations • 26 Sep 2024 • Zhangpu Li, Changhong Zou, Suxue Ma, Zhicheng Yang, Chen Du, YouBao Tang, Zhenjie Cao, Ning Zhang, Jui-Hsin Lai, Ruei-Sung Lin, Yuan Ni, Xingzhi Sun, Jing Xiao, Jieke Hou, Kai Zhang, Mei Han
In our online medical consultation scenario, a doctor responds to the texts and images provided by a patient in multiple rounds to diagnose her/his health condition, forming a multi-turn multimodal medical dialogue format.
no code implementations • 28 May 2024 • Wei Zhu, Aaron Xuxiang Tian, Congrui Yin, Yuan Ni, Xiaoling Wang, Guotong Xie
Thus, we propose to learn the idiosyncratic activation functions for prompt generators automatically with the help of rational functions.
1 code implementation • 4 Jan 2024 • Wei Zhu, Wenfeng Li, Xing Tian, Pengfei Wang, Xiaoling Wang, Jin Chen, Yuanbin Wu, Yuan Ni, Guotong Xie
In this work, we propose a novel task, Text2MDT, to explore the automatic extraction of MDTs from medical texts such as medical guidelines and textbooks.
4 code implementations • 2 Oct 2023 • Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Bingxiang He, Wei Zhu, Yuan Ni, Guotong Xie, Ruobing Xie, Yankai Lin, Zhiyuan Liu, Maosong Sun
Our work validates the effectiveness of scaled AI feedback data in constructing strong open-source chat language models, serving as a solid foundation for future feedback learning research.
1 code implementation • 7 May 2023 • Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, Xipeng Qiu
To train UDR, we cast various tasks' training signals into a unified list-wise ranking formulation by language model's feedback.
1 code implementation • Findings (ACL) 2022 • Tianxiang Sun, Xiangyang Liu, Wei Zhu, Zhichao Geng, Lingling Wu, Yilong He, Yuan Ni, Guotong Xie, Xuanjing Huang, Xipeng Qiu
Previous works usually adopt heuristic metrics such as the entropy of internal outputs to measure instance difficulty, which suffers from generalization and threshold-tuning.
2 code implementations • ACL 2022 • Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei LI, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Semantic Similarity
on CHIP-STS
no code implementations • NAACL 2021 • Wei Zhu, Yuan Ni, Xiaoling Wang, Guotong Xie
In developing an online question-answering system for the medical domains, natural language inference (NLI) models play a central role in question matching and intention detection.
no code implementations • ACL 2021 • Wei Zhu, Xipeng Qiu, Yuan Ni, Guotong Xie
Ablation study demonstrates the necessity of our search space design and the effectiveness of our search method.
3 code implementations • 4 Sep 2020 • Wei Zhu, Xiaoling Wang, Xipeng Qiu, Yuan Ni, Guotong Xie
Though the transformer architectures have shown dominance in many natural language understanding tasks, there are still unsolved issues for the training of transformer models, especially the need for a principled way of warm-up which has shown importance for stable training of a transformer, as well as whether the task at hand prefer to scale the attention product or not.
no code implementations • WS 2019 • Xiepeng Li, Zhexi Zhang, Wei Zhu, Zheng Li, Yuan Ni, Peng Gao, Junchi Yan, Guotong Xie
We have experimented both (a) improving the fine-tuning of pre-trained language models on a task with a small dataset size, by leveraging datasets of similar tasks; and (b) incorporating the distributional representations of a KG onto the representations of pre-trained language models, via simply concatenation or multi-head attention.
Ranked #17 on
Common Sense Reasoning
on ReCoRD
no code implementations • WS 2019 • Wei Zhu, Xiaofeng Zhou, Keqiang Wang, Xun Luo, Xiepeng Li, Yuan Ni, Guotong Xie
Transfer learning from the NLI task to the RQE task is also experimented, which proves to be useful in improving the results of fine-tuning MT-DNN large.