9 citations · 25 across the 10 of their papers we have counts for
11 papers
MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis
Hongjie Cai, Nan Song, Zengzhi Wang +7
Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple…
BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework
Heng Yang, Ke Li
Text augmentation is an effective technique for addressing the problem of insufficient data in natural language processing. However, existing text augmentation methods tend to focu…
PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis
Heng Yang, Chen Zhang, Ke Li
The advancement of aspect-based sentiment analysis (ABSA) has urged the lack of a user-friendly framework that can largely lower the difficulty of reproducing state-of-the-art ABSA…
MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet
Nan Wang, Shaohui Lin, Xiaoxiao Li +4
U-Nets have achieved tremendous success in medical image segmentation. Nevertheless, it may suffer limitations in global (long-range) contextual interactions and edge-detail preser…
LSA: Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation
Heng Yang, Ke Li
Aspect sentiment coherency is an intriguing yet underexplored topic in the field of aspect-based sentiment classification. This concept reflects the common pattern where adjacent a…
Unsupervised Discriminative Learning of Sounds for Audio Event Classification
Sascha Hornauer, Ke Li, Stella X. Yu +2
Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet. While this process allows knowledge transf…
A Parallelizable Lattice Rescoring Strategy with Neural Language Models
Ke Li, Daniel Povey, Sanjeev Khudanpur
This paper proposes a parallel computation strategy and a posterior-based lattice expansion algorithm for efficient lattice rescoring with neural language models (LMs) for automati…
Neural Language Modeling With Implicit Cache Pointers
Ke Li, Daniel Povey, Sanjeev Khudanpur
A cache-inspired approach is proposed for neural language models (LMs) to improve long-range dependency and better predict rare words from long contexts. This approach is a simpler…
Efficient MDI Adaptation for n-gram Language Models
Ruizhe Huang, Ke Li, Ashish Arora +2
This paper presents an efficient algorithm for n-gram language model adaptation under the minimum discrimination information (MDI) principle, where an out-of-domain language model…
The JHU Multi-Microphone Multi-Speaker ASR System for the CHiME-6 Challenge
Ashish Arora, Desh Raj, Aswin Shanmugam Subramanian +7
This paper summarizes the JHU team's efforts in tracks 1 and 2 of the CHiME-6 challenge for distant multi-microphone conversational speech diarization and recognition in everyday h…
Speaker Adaptation for End-to-End CTC Models
Ke Li, Jinyu Li, Yong Zhao +2
We propose two approaches for speaker adaptation in end-to-end (E2E) automatic speech recognition systems. One is Kullback-Leibler divergence (KLD) regularization and the other is…