55 citations · 136 across the 11 of their papers we have counts for
11 papers
Graph Sequential Network for Reasoning over Sequences
Ming Tu, Jing Huang, Xiaodong He +1
Recently Graph Neural Network (GNN) has been applied successfully to various NLP tasks that require reasoning, such as multi-hop machine reading comprehension. In this paper, we co…
Speaker-invariant Affective Representation Learning via Adversarial Training
Haoqi Li, Ming Tu, Jing Huang +2
Representation learning for speech emotion recognition is challenging due to labeled data sparsity issue and lack of gold standard references. In addition, there is much variabilit…
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Ming Tu, Kevin Huang, Guangtao Wang +3
Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining th…
Multiple instance learning with graph neural networks
Ming Tu, Jing Huang, Xiaodong He +1
Multiple instance learning (MIL) aims to learn the mapping between a bag of instances and the bag-level label. In this paper, we propose a new end-to-end graph neural network (GNN)…
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
Ming Tu, Guangtao Wang, Jing Huang +3
Multi-hop reading comprehension (RC) across documents poses new challenge over single-document RC because it requires reasoning over multiple documents to reach the final answer. I…
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen +43
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which…