80 citations · 317 across the 48 of their papers we have counts for
26 papers · 1 filter
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…
Large Language Models Can Self-Improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou +4
Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improv…
Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Dongha Lee, Jiaming Shen, Seonghyeon Lee +3
Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications. To dynamically incorporate new topic info…
Towards a Unified Multi-Dimensional Evaluator for Text Generation
Ming Zhong, Yang Liu, Da Yin +6
Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimen…
Few-shot Text Classification with Dual Contrastive Consistency
Liwen Sun, Jiawei Han
In this paper, we explore how to utilize pre-trained language model to perform few-shot text classification where only a few annotated examples are given for each class. Since usin…
TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter
Xinyang Zhang, Yury Malkov, Omar Florez +4
Pre-trained language models (PLMs) are fundamental for natural language processing applications. Most existing PLMs are not tailored to the noisy user-generated text on social medi…
MentorGNN: Deriving Curriculum for Pre-Training GNNs
Dawei Zhou, Lecheng Zheng, Dongqi Fu +2
Graph pre-training strategies have been attracting a surge of attention in the graph mining community, due to their flexibility in parameterizing graph neural networks (GNNs) witho…
Unsupervised Key Event Detection from Massive Text Corpora
Yunyi Zhang, Fang Guo, Jiaming Shen +1
Automated event detection from news corpora is a crucial task towards mining fast-evolving structured knowledge. As real-world events have different granularities, from the top-lev…
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation
Jiaxin Huang, Yu Meng, Jiawei Han
We study the problem of few-shot Fine-grained Entity Typing (FET), where only a few annotated entity mentions with contexts are given for each entity type. Recently, prompt-based t…
TeKo: Text-Rich Graph Neural Networks with External Knowledge
Zhizhi Yu, Di Jin, Jianguo Wei +5
Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i.e., networks). Typical GNNs and their variants follow a m…
Schema-Guided Event Graph Completion
Hongwei Wang, Zixuan Zhang, Sha Li +5
We tackle a new task, event graph completion, which aims to predict missing event nodes for event graphs. Existing link prediction or graph completion methods have difficulty deali…
All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass
Jiaxin Huang, Tianqi Liu, Jialu Liu +3
Multi-Task Learning (MTL) models have shown their robustness, effectiveness, and efficiency for transferring learned knowledge across tasks. In real industrial applications such as…
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks
Bowen Jin, Yu Zhang, Qi Zhu +1
Representation learning on networks aims to derive a meaningful vector representation for each node, thereby facilitating downstream tasks such as link prediction, node classificat…
CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision
Yuning Mao, Ming Zhong, Jiawei Han
Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers. Previous efforts on curating scientific TLDR datasets failed to scale up due to the…
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds
Yu Zhang, Yu Meng, Xuan Wang +2
Discovering latent topics from text corpora has been studied for decades. Many existing topic models adopt a fully unsupervised setting, and their discovered topics may not cater t…
Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
Yu Meng, Chenyan Xiong, Payal Bajaj +4
We present a new framework AMOS that pretrains text encoders with an Adversarial learning curriculum via a Mixture Of Signals from multiple auxiliary generators. Following ELECTRA-…
Shift-Robust Node Classification via Graph Adversarial Clustering
Qi Zhu, Chao Zhang, Chanyoung Park +2
Graph Neural Networks (GNNs) are de facto node classification models in graph structured data. However, during testing-time, these algorithms assume no data shift, i.e., $\Pr_\text…
TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations
Minhao Jiang, Xiangchen Song, Jieyu Zhang +1
Taxonomies are fundamental to many real-world applications in various domains, serving as structural representations of knowledge. To deal with the increasing volume of new concept…
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations
Yu Meng, Yunyi Zhang, Jiaxin Huang +2
Topic models have been the prominent tools for automatic topic discovery from text corpora. Despite their effectiveness, topic models suffer from several limitations including the…
P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization
Sha Li, Liyuan Liu, Yiqing Xie +2
We propose P4E, an identify-and-localize event detection framework that integrates the best of few-shot prompting and structured prediction. Our framework decomposes event detectio…