Papers by Fangli Xu
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph (2022.aacl-main)
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| Challenge: | Existing knowledge graph question answering methods only search for the answer in a large knowledge graph. |
| Approach: | They propose to partition retrieved knowledge subgraphs into smaller sub-KSGs and then use a graph-augmented learning to rank method to select the top-ranked sub-kSGs. |
| Outcome: | The proposed method can capture global interactions in question and subgraphs and local interactions on the full KSG and top-ranked sub-KSGs respectively. |
Word Mover’s Embedding: From Word2Vec to Document Embedding (D18-1)
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Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
| Challenge: | Recent work has demonstrated that Word Mover’s Distance (WMD) that aligns semantically similar words yields unprecedented KNN classification accuracy. |
| Approach: | They propose a Word Mover’s Distance (WMD) method that aligns semantically similar words to generate unsupervised sentences or documents embeddings. |
| Outcome: | The proposed method consistently outperforms state-of-the-art techniques on 9 benchmark text classification datasets and 22 textual similarity tasks. |
Constructing contrastive samples via summarization for text classification with limited annotations (2021.findings-emnlp)
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| Challenge: | Various contrastive learning methods have been developed and lead to state-of-the-art performance in many computer vision tasks. |
| Approach: | They propose a method to construct efficient contrastive samples using text summarization to gain better representations of text classification tasks with limited annotations. |
| Outcome: | The proposed framework gains better representations on text classification tasks with limited annotations and is compared with existing methods on real-world text classification datasets. |
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020.findings-emnlp)
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| Challenge: | Graph2Tree model encodes graph-structured input and decodes tree-structures output. |
| Approach: | They propose a novel Graph-to-Tree Neural Network consisting of a graph encoder and a hierarchical tree decoder that encodes an augmented graph-structured input and decodes a tree-structure-output. |
| Outcome: | The proposed model outperforms or matches the performance of other state-of-the-art models on two problems, neural semantic parsing and math word problem. |
Feeding What You Need by Understanding What You Learned (2022.acl-long)
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| Challenge: | Existing research on machine reading comprehension rely heavily on large-size models and corpus to improve performance. |
| Approach: | They propose a framework that assesses model capabilities in an explainable and multi-dimensional manner. |
| Outcome: | The proposed framework achieves an 11.22% / 8.71% improvement of EM / F1 on MRC tasks. |