142 citations · 405 across the 18 of their papers we have counts for
6 papers · 1 filter
Feeding What You Need by Understanding What You Learned
Xiaoqiang Wang, Bang Liu, Fangli Xu +3
Machine Reading Comprehension (MRC) reveals the ability to understand a given text passage and answer questions based on it. Existing research works in MRC rely heavily on large-si…
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks
Hanning Gao, Lingfei Wu, Hongyun Zhang +4
Considering a collection of RDF triples, the RDF-to-text generation task aims to generate a text description. Most previous methods solve this task using a sequence-to-sequence mod…
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph
Hanning Gao, Lingfei Wu, Po Hu +3
Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods fi…
Constructing Contrastive samples via Summarization for Text Classification with limited annotations
Yangkai Du, Tengfei Ma, Lingfei Wu +4
Contrastive Learning has emerged as a powerful representation learning method and facilitates various downstream tasks especially when supervised data is limited. How to construct…
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…
Word Mover's Embedding: From Word2Vec to Document Embedding
Lingfei Wu, Ian E. H. Yen, Kun Xu +5
While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised…