2.3k citations · 2.5k across the 19 of their papers we have counts for
19 papers
SpanDrop: Simple and Effective Counterfactual Learning for Long Sequences
Peng Qi, Guangtao Wang, Jing Huang
Distilling supervision signal from a long sequence to make predictions is a challenging task in machine learning, especially when not all elements in the input sequence contribute…
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs
Chao Shang, Guangtao Wang, Peng Qi +1
Question answering over temporal knowledge graphs (KGs) efficiently uses facts contained in a temporal KG, which records entity relations and when they occur in time, to answer nat…
Semantic Categorization of Social Knowledge for Commonsense Question Answering
Gengyu Wang, Xiaochen Hou, Diyi Yang +2
Large pre-trained language models (PLMs) have led to great success on various commonsense question answering (QA) tasks in an end-to-end fashion. However, little attention has been…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System
Zichuan Lin, Jing Huang, Bowen Zhou +2
Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on dialog systems and found that improvement on individual components (e.g., NLU, policy) in prior work may…
UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks
Jing Huang, Jie Yang
Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domain…
Residual Enhanced Multi-Hypergraph Neural Network
Jing Huang, Xiaolin Huang, Jie Yang
Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various research domains. Meanwh…
Conversational AI Systems for Social Good: Opportunities and Challenges
Peng Qi, Jing Huang, Youzheng Wu +2
Conversational artificial intelligence (ConvAI) systems have attracted much academic and commercial attention recently, making significant progress on both fronts. However, little…
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification
Xiaochen Hou, Peng Qi, Guangtao Wang +4
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), b…
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
Wenxuan Zhou, Kevin Huang, Tengyu Ma +1
Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair o…
Multi-hop Attention Graph Neural Network
Guangtao Wang, Rex Ying, Jing Huang +1
Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is co…
Inductive Learning on Commonsense Knowledge Graph Completion
Bin Wang, Guangtao Wang, Jing Huang +3
Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. However, most existing CKG completion methods focus on t…
Entity and Evidence Guided Relation Extraction for DocRED
Kevin Huang, Guangtao Wang, Tengyu Ma +1
Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document. In this paper, we pro-pose a…
Speaker Diarization with Lexical Information
Tae Jin Park, Kyu J. Han, Jing Huang +4
This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition. We propose a speaker diarization system that c…
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…
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
Yun Tang, Jing Huang, Guangtao Wang +2
Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE. However, N…
Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification
Xiaochen Hou, Jing Huang, Guangtao Wang +2
Aspect-level sentiment classification aims to identify the sentiment polarity towards a specific aspect term in a sentence. Most current approaches mainly consider the semantic inf…
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)…
Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification
Yun Tang, Guohong Ding, Jing Huang +2
This paper aims to improve the widely used deep speaker embedding x-vector model. We propose the following improvements: (1) a hybrid neural network structure using both time delay…