217 citations · 2.1k across the 107 of their papers we have counts for
17 papers · 1 filter
Stronger Transformers for Neural Multi-Hop Question Generation
Devendra Singh Sachan, Lingfei Wu, Mrinmaya Sachan +1
Prior work on automated question generation has almost exclusively focused on generating simple questions whose answers can be extracted from a single document. However, there is a…
Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Hanlu Wu, Tengfei Ma, Lingfei Wu +2
Evaluation of a document summarization system has been a critical factor to impact the success of the summarization task. Previous approaches, such as ROUGE, mainly consider the in…
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Hanlu Wu, Tengfei Ma, Lingfei Wu +1
Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-ex…
Deep Graph Matching and Searching for Semantic Code Retrieval
Xiang Ling, Lingfei Wu, Saizhuo Wang +6
Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses na…
Technical Question Answering across Tasks and Domains
Wenhao Yu, Lingfei Wu, Yu Deng +4
Building automatic technical support system is an important yet challenge task. Conceptually, to answer a user question on a technical forum, a human expert has to first retrieve r…
Multilevel Graph Matching Networks for Deep Graph Similarity Learning
Xiang Ling, Lingfei Wu, Saizhuo Wang +5
While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph…
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Xiaojie Guo, Liang Zhao, Zhao Qin +3
Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning. However, learning the dis…
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
Yu Chen, Lingfei Wu, Mohammed J. Zaki
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedd…
A Multi-Perspective Architecture for Semantic Code Search
Rajarshi Haldar, Lingfei Wu, Jinjun Xiong +1
The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositori…
Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward
Luyang Huang, Lingfei Wu, Lu Wang
Sequence-to-sequence models for abstractive summarization have been studied extensively, yet the generated summaries commonly suffer from fabricated content, and are often found to…
Crossing Variational Autoencoders for Answer Retrieval
Wenhao Yu, Lingfei Wu, Qingkai Zeng +3
Answer retrieval is to find the most aligned answer from a large set of candidates given a question. Learning vector representations of questions/answers is the key factor. Questio…
Improved Automatic Summarization of Subroutines via Attention to File Context
Sakib Haque, Alexander LeClair, Lingfei Wu +1
Software documentation largely consists of short, natural language summaries of the subroutines in the software. These summaries help programmers quickly understand what a subrouti…
Improved Code Summarization via a Graph Neural Network
Alexander LeClair, Sakib Haque, Lingfei Wu +1
Automatic source code summarization is the task of generating natural language descriptions for source code. Automatic code summarization is a rapidly expanding research area, espe…
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem
Shucheng Li, Lingfei Wu, Shiwei Feng +3
The celebrated Seq2Seq technique and its numerous variants achieve excellent performance on many tasks such as neural machine translation, semantic parsing, and math word problem s…
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting which is to g…
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing. As an…
Social Centralization and Semantic Collapse: Hyperbolic Embeddings of Networks and Text
Linzhuo Li, Lingfei Wu, James Allen Evans
Modern advances in transportation and communication technology from airplanes to the internet alongside global expansions of media, migration, and trade have made the modern world…