no code implementations • EMNLP 2020 • Yuanmeng Yan, Keqing He, Hong Xu, Sihong Liu, Fanyu Meng, Min Hu, Weiran Xu
Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit.
no code implementations • 18 Apr 2024 • Jie Ma, Min Hu, Pinghui Wang, Wangchun Sun, Lingyun Song, Hongbin Pei, Jun Liu, Youtian Du
The former leads to a large, diverse test space, while the latter results in a comprehensive robustness evaluation on rare, frequent, and overall questions.
Audio-visual Question Answering Audio-Visual Question Answering (AVQA) +3
no code implementations • 21 Nov 2023 • Ziqi Ye, Limin Huang, Yongji Wu, Min Hu
The combination of artificial intelligence and augmented reality technology has also become a future development trend.
1 code implementation • 6 May 2023 • Jie Ma, Pinghui Wang, Zewei Wang, Dechen Kong, Min Hu, Ting Han, Jun Liu
Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering).
Extractive Question-Answering Machine Reading Comprehension +2
no code implementations • 29 Mar 2023 • Min Hu, Zhizhong Tan, Bin Liu, Guosheng Yin
This study aims to address the challenges of futures price prediction in high-frequency trading (HFT) by proposing a continuous learning factor predictor based on graph neural networks.
no code implementations • 16 Feb 2023 • Jian Wu, Zhuo Chen, Min Hu, Xiong Xiao, Jinyu Li
Speaker change detection (SCD) is an important feature that improves the readability of the recognized words from an automatic speech recognition (ASR) system by breaking the word sequence into paragraphs at speaker change points.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 6 Feb 2022 • Min Hu, Yi Wang, Xiaowei Feng, Shengchen Zhou, Zhaoyu Wu, Yuan Qin
The experiments showed that in benchmark datasets RADTD possessed higher accuracy and robustness than recurrence qualification analysis and extreme learning machine autoencoder, respectively, and that RADTD accurately detected the occurrence of tunneling settlement accidents, indicating its remarkable performance in accuracy and robustness.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Fanyu Meng, Junlan Feng, Danping Yin, Si Chen, Min Hu
Syntactic information is essential for both sentiment analysis(SA) and aspect-based sentiment analysis(ABSA).
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
no code implementations • ACL 2020 • Yi Huang, Junlan Feng, Min Hu, Xiaoting Wu, Xiaoyu Du, Shuo Ma
The state-of-the-art accuracy for DST is below 50{\%} for a multi-domain dialogue task.
no code implementations • 31 Jan 2020 • Chuang Wang, Ruimin Hu, Min Hu, Jiang Liu, Ting Ren, Shan He, Ming Jiang, Jing Miao
And we validate our method on the Aff-Wild2 datasets released by the Challenge.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 29 Nov 2018 • Xiaohua Wang, Muzi Peng, Lijuan Pan, Min Hu, Chunhua Jin, Fuji Ren
In this paper, a two-level attention with two-stage multi-task learning (2Att-2Mt) framework is proposed for facial emotion estimation on only static images.
no code implementations • 4 Nov 2018 • Kai Hu, Zhijian Ou, Min Hu, Junlan Feng
Conditional random fields (CRFs) have been shown to be one of the most successful approaches to sequence labeling.