no code implementations • NAACL (WNU) 2022 • Achyutarama Ganti, Steven Wilson, Zexin Ma, Xinyan Zhao, Rong Ma
Narratives have been shown to be an effective way to communicate health risks and promote health behavior change, and given the growing amount of health information being shared on social media, it is crucial to study health-related narratives in social media.
no code implementations • SMM4H (COLING) 2020 • V.G.Vinod Vydiswaran, Deahan Yu, Xinyan Zhao, Ermioni Carr, Jonathan Martindale, Jingcheng Xiao, Noha Ghannam, Matteo Althoen, Alexis Castellanos, Neel Patel, Daniel Vasquez
The team from the University of Michigan participated in three tasks in the Social Media Mining for Health Applications (#SMM4H) 2020 shared tasks – on detecting mentions of adverse effects (Task 2), extracting and normalizing them (Task 3), and detecting mentions of medication abuse (Task 4).
1 code implementation • 31 Oct 2023 • Yohan Jo, Xinyan Zhao, Arijit Biswas, Nikoletta Basiou, Vincent Auvray, Nikolaos Malandrakis, Angeliki Metallinou, Alexandros Potamianos
While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Jiazhao Li, Corey Lester, Xinyan Zhao, Yuting Ding, Yun Jiang, V. G. Vinod Vydiswaran
We propose a novel machine translation-based approach, PharmMT, to automatically and reliably simplify prescription directions into patient-friendly language, thereby significantly reducing pharmacist workload.
no code implementations • dialdoc (ACL) 2022 • Xinyan Zhao, Bin He, Yasheng Wang, Yitong Li, Fei Mi, Yajiao Liu, Xin Jiang, Qun Liu, Huanhuan Chen
With the advances in deep learning, tremendous progress has been made with chit-chat dialogue systems and task-oriented dialogue systems.
1 code implementation • EACL 2021 • Xinyan Zhao, Haibo Ding, Zhe Feng
Instead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules.
no code implementations • 12 Jan 2021 • Jiele Wu, Chau-Wai Wong, Xinyan Zhao, Xianpeng Liu
For subjective features such as semantic connotation, online workers, known for optimizing their hourly earnings, tend to deteriorate in the quality of their responses as they work longer.
1 code implementation • 16 Dec 2020 • Xinyan Zhao, V. G. Vinod Vydiswaran
Natural language explanations (NLEs) are a special form of data annotation in which annotators identify rationales (most significant text tokens) when assigning labels to data instances, and write out explanations for the labels in natural language based on the rationales.
no code implementations • 21 Oct 2020 • Xinyan Zhao, LiangWei Chen, Huanhuan Chen
Most research on disease diagnosis dialogue systems highly rely on data-driven methods and statistical features, lacking profound comprehension of symptom-disease relations and symptom-symptom relations.
no code implementations • 30 Jun 2020 • Xinyan Zhao, Xiao Feng, Haoming Zhong, Jun Yao, Huanhuan Chen
CAR-Transformer (1) revises each condition value based on the whole conversation and original conditions values, and (2) it encodes the revised conditions and utilizes the conditions embedding to select an answer.
no code implementations • WS 2019 • Xinyan Zhao, Deahan Yu, V.G.Vinod Vydiswaran
Identifying mentions of medical concepts in social media is challenging because of high variability in free text.
no code implementations • 23 May 2019 • Jie Wang, Xinyan Zhao
With rapid development of neural networks, deep-learning has been extended to various natural language generation fields, such as machine translation, dialogue generation and even literature creation.