Selected Publications

Fuzzy Logic based Logical Query Answering on Knowledge Graph
AAAI Conference on Artificial Intelligence (AAAI 2022)
We propose FuzzQE, a fuzzy logic based logical query embedding framework for answering FOL queries over KGs. FuzzQE define logical operators in a principled and learningfree manner, which could be trained with only KG without any complex queries.
Relation-Guided Pre-Training for Open-Domain Question Answering
The Conference on Empirical Methods in Natural Language Processing (EMNLP-Finding, 2021)
We propose RGPT-QA to synthesize QA pairs from relation triplets in WikiData and WikiPedia for pre-training Open-Domain QA Model and improves the QA performance, especially for questions with long-tail relations.
GPT-GNN: Generative Pre-Training of Graph Neural Networks
The Conference on Knowledge Discovery and Data Mining (KDD 2020)
We introduce a self-supervised graph generation task to pre-train GNN. We factorize the likelihood of graph generation into two components: 1) attribute generation, and 2) edge generation, without lossing mutual dependency.
Heterogeneous Graph Transformer
The Web Conference (WWW 2020)
We present the Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous (nodes and edges have multiple types) and dynamic graphs. HGT could automatically learns important meta-paths for different downstream tasks.
Improving Neural Language Generation with Spectrum Control
The International Conference on Learning Representations (ICLR 2020)
We propose a novel spectrum control approach to address this degeneration problem. The core idea of our method is to directly guide the spectra training of the output embedding matrix with a slow-decaying singular value prior distribution through a reparameterization framework.
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
The Conference on Neural Information Processing Systems (NeurIPS 2019)
We propose LAyer-Dependent ImportancE Sampling (LADIES). Based on the sampled nodes in the upper layer, LADIES selects their neighborhood nodes, compute the importance probability accordingly and samples a fixed number of nodes within them.
Few-Shot Representation Learning for Out-Of-Vocabulary Words
The Conference of the Association for Computational Linguistics (ACL 2019)
We formulate the learning of OOV embedding as a few-shot regression problem by predicting an oracle embedding vector (defined as embedding trained with abundant observations) based on only K contexts. Specifically, we use Model-Agnostic Meta-Learning (MAML) for adapting a hierachical Transformer to the new corpus fast and robustly.
Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm
The Web Conference (WWW 2019)
We propose a novel framework for pairwise learning-to-rank. Our algorithm, Unbiased LambdaMART can jointly estimate the biases at click positions and the biases at unclick positions, and learn an unbiased ranker.
Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification
The Web Conference (WWW 2019, Best Full Paper Award)
We employ emoji prediction task as the instrument to learn both the cross-language and language-specific sentiment patterns in different languages.
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
The Conference on Web Search and Data Mining (WSDM 2018).
We designed a Hybrid Attention Networkss(HAN) to predict the stock trend based on the sequence of recent related news, with self-paced learning mechanism to guide efficient learning.