80 citations · 317 across the 48 of their papers we have counts for
48 papers
Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification
Sha Li, Ruining Zhao, Manling Li +3
Event schemas are a form of world knowledge about the typical progression of events. Recent methods for event schema induction use information extraction systems to construct a lar…
ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervision
Ming Zhong, Siru Ouyang, Minhao Jiang +4
Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design. Despite the importanc…
Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers
Yu Zhang, Bowen Jin, Xiusi Chen +4
Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descript…
Explaining and Adapting Graph Conditional Shift
Qi Zhu, Yizhu Jiao, Natalia Ponomareva +2
Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution sh…
Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
Fangzhi Xu, Qika Lin, Jiawei Han +3
Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs)…
Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
Pengcheng Jiang, Shivam Agarwal, Bowen Jin +3
The mission of open knowledge graph (KG) completion is to draw new findings from known facts. Existing works that augment KG completion require either (1) factual triples to enlarg…
Patton: Language Model Pretraining on Text-Rich Networks
Bowen Jin, Wentao Zhang, Yu Zhang +4
A real-world text corpus sometimes comprises not only text documents but also semantic links between them (e.g., academic papers in a bibliographic network are linked by citations…
Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding
Susik Yoon, Dongha Lee, Yunyi Zhang +1
Unsupervised discovery of stories with correlated news articles in real-time helps people digest massive news streams without expensive human annotations. A common approach of the…
Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation
Da Yin, Xiao Liu, Fan Yin +4
Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses. Existing methods either ma…
PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training
Yunyi Zhang, Minhao Jiang, Yu Meng +2
Weakly-supervised text classification trains a classifier using the label name of each target class as the only supervision, which largely reduces human annotation efforts. Most ex…
OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing
Tanay Komarlu, Minhao Jiang, Xuan Wang +1
Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, is a basic but important task for knowledge extraction from un…
MEGClass: Extremely Weakly Supervised Text Classification via Mutually-Enhancing Text Granularities
Priyanka Kargupta, Tanay Komarlu, Susik Yoon +2
Text classification is essential for organizing unstructured text. Traditional methods rely on human annotations or, more recently, a set of class seed words for supervision, which…
GLEN: General-Purpose Event Detection for Thousands of Types
Qiusi Zhan, Sha Li, Kathryn Conger +3
The progress of event extraction research has been hindered by the absence of wide-coverage, large-scale datasets. To make event extraction systems more accessible, we build a gene…
Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
Bowen Jin, Yu Zhang, Yu Meng +1
Edges in many real-world social/information networks are associated with rich text information (e.g., user-user communications or user-product reviews). However, mainstream network…
PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream
Susik Yoon, Hou Pong Chan, Jiawei Han
Summarizing text-rich documents has been long studied in the literature, but most of the existing efforts have been made to summarize a static and predefined multi-document set. Wi…
Augmenting Zero-Shot Dense Retrievers with Plug-in Mixture-of-Memories
Suyu Ge, Chenyan Xiong, Corby Rosset +3
In this paper we improve the zero-shot generalization ability of language models via Mixture-Of-Memory Augmentation (MoMA), a mechanism that retrieves augmentation documents from m…
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
Yu Zhang, Bowen Jin, Qi Zhu +2
Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interes…
Representation Deficiency in Masked Language Modeling
Yu Meng, Jitin Krishnan, Sinong Wang +6
Masked Language Modeling (MLM) has been one of the most prominent approaches for pretraining bidirectional text encoders due to its simplicity and effectiveness. One notable concer…
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts
Yu Zhang, Yunyi Zhang, Martin Michalski +3
Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract dis…
Entity Set Co-Expansion in StackOverflow
Yu Zhang, Yunyi Zhang, Yucheng Jiang +5
Given a few seed entities of a certain type (e.g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as…
Open Relation and Event Type Discovery with Type Abstraction
Sha Li, Heng Ji, Jiawei Han
Conventional closed-world information extraction (IE) approaches rely on human ontologies to define the scope for extraction. As a result, such approaches fall short when applied t…
Open-Vocabulary Argument Role Prediction for Event Extraction
Yizhu Jiao, Sha Li, Yiqing Xie +3
The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies…
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
Yu Meng, Martin Michalski, Jiaxin Huang +3
Recent studies have revealed the intriguing few-shot learning ability of pretrained language models (PLMs): They can quickly adapt to a new task when fine-tuned on a small amount o…
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
Jianhao Shen, Chenguang Wang, Ye Yuan +5
This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM…