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Complex Reading Comprehension Through Question Decomposition
Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari
Multi-hop reading comprehension requires not only the ability to reason over raw text but also the ability to combine multiple evidence. We propose a novel learning approach that h…
Towards Relation Extraction From Speech
Tongtong Wu, Guitao Wang, Jinming Zhao +4
Relation extraction typically aims to extract semantic relationships between entities from the unstructured text. One of the most essential data sources for relation extraction is…
Vision Transformers provably learn spatial structure
Samy Jelassi, Michael E. Sander, Yuanzhi Li
Vision Transformers (ViTs) have achieved comparable or superior performance than Convolutional Neural Networks (CNNs) in computer vision. This empirical breakthrough is even more r…
Teaching Neural Module Networks to Do Arithmetic
Jiayi Chen, Xiao-Yu Guo, Yuan-Fang Li +1
Answering complex questions that require multi-step multi-type reasoning over raw text is challenging, especially when conducting numerical reasoning. Neural Module Networks(NMNs),…
Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis
Jiawen Kang, Hongyang Du, Zonghang Li +4
Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G net…
Towards Understanding Mixture of Experts in Deep Learning
Zixiang Chen, Yihe Deng, Yue Wu +2
The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlled by a router, has achieved great success in deep learning. However, the understanding of such architecture…
Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning
Tao He, Lianli Gao, Jingkuan Song +1
Scene graph generation (SGG) is a fundamental task aimed at detecting visual relations between objects in an image. The prevailing SGG methods require all object classes to be give…
Neural Topic Modeling with Deep Mutual Information Estimation
Kang Xu, Xiaoqiu Lu, Yuan-fang Li +5
The emerging neural topic models make topic modeling more easily adaptable and extendable in unsupervised text mining. However, the existing neural topic models is difficult to ret…