no code implementations • 15 Nov 2023 • Minqian Liu, Ying Shen, Zhiyang Xu, Yixin Cao, Eunah Cho, Vaibhav Kumar, Reza Ghanadan, Lifu Huang
Natural Language Generation (NLG) typically involves evaluating the generated text in various aspects (e. g., consistency and naturalness) to obtain a comprehensive assessment.
1 code implementation • 8 Oct 2023 • Jingyuan Qi, Minqian Liu, Ying Shen, Zhiyang Xu, Lifu Huang
Automatically generating scripts (i. e. sequences of key steps described in text) from video demonstrations and reasoning about the subsequent steps are crucial to the modern AI virtual assistants to guide humans to complete everyday tasks, especially unfamiliar ones.
1 code implementation • 26 May 2023 • Minqian Liu, Lifu Huang
Class-incremental learning (CIL) aims to develop a learning system that can continually learn new classes from a data stream without forgetting previously learned classes.
1 code implementation • 24 May 2023 • Jingyuan Qi, Zhiyang Xu, Ying Shen, Minqian Liu, Di Jin, Qifan Wang, Lifu Huang
Chain-of-Thought (CoT) prompting enables large language models to solve complex reasoning problems by generating intermediate steps.
no code implementations • 24 May 2023 • Barry Menglong Yao, Yu Chen, Qifan Wang, Sijia Wang, Minqian Liu, Zhiyang Xu, Licheng Yu, Lifu Huang
We propose attribute-aware multimodal entity linking, where the input is a mention described with a text and image, and the goal is to predict the corresponding target entity from a multimodal knowledge base (KB) where each entity is also described with a text description, a visual image and a set of attributes and values.
no code implementations • 24 May 2023 • Xiaochu Li, Minqian Liu, Zhiyang Xu, Lifu Huang
To solve these challenges, we propose joint biomedical entity linking and event extraction by regarding the event structures and entity references in knowledge bases as latent variables and updating the two task-specific models in a hard Expectation-Maximization (EM) fashion: (1) predicting the missing variables for each partially annotated dataset based on the current two task-specific models, and (2) updating the parameters of each model on the corresponding pseudo completed dataset.
1 code implementation • COLING 2022 • Minqian Liu, Shiyu Chang, Lifu Huang
Lifelong event detection aims to incrementally update a model with new event types and data while retaining the capability on previously learned old types.
1 code implementation • Neurocomputing 2021 • Minqian Liu, Lizhao Liu, Junyi Cao, Qing Du
To alleviate this issue, label embedding frameworks are proposed to adopt the label-to-text attention that directly uses label information to construct the text representation for more efficient text classification.
Ranked #2 on Multi-Label Text Classification on AAPD (Micro F1 metric)