607 citations · 2.2k across the 78 of their papers we have counts for
22 papers · 2 filters
Few-Shot Named Entity Recognition: A Comprehensive Study
Jiaxin Huang, Chunyuan Li, Krishan Subudhi +6
This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of in-domain labeled data is available. Based upon recent…
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
Yanru Qu, Dinghan Shen, Yelong Shen +3
Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, de…
Text Classification Using Label Names Only: A Language Model Self-Training Approach
Yu Meng, Yunyi Zhang, Jiaxin Huang +4
Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. H…
CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring
Jiaxin Huang, Yiqing Xie, Yu Meng +2
Taxonomy is not only a fundamental form of knowledge representation, but also crucial to vast knowledge-rich applications, such as question answering and web search. Most existing…
Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding
Jiaxin Huang, Yu Meng, Fang Guo +2
Aspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner. It has in general two sub-tasks: (i) extracting aspects…
Hierarchical Metadata-Aware Document Categorization under Weak Supervision
Yu Zhang, Xiusi Chen, Yu Meng +1
Categorizing documents into a given label hierarchy is intuitively appealing due to the ubiquity of hierarchical topic structures in massive text corpora. Although related studies…
Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
Yuning Mao, Xiang Ren, Heng Ji +1
Despite significant progress, state-of-the-art abstractive summarization methods are still prone to hallucinate content inconsistent with the source document. In this paper, we pro…
On the Transformer Growth for Progressive BERT Training
Xiaotao Gu, Liyuan Liu, Hongkun Yu +3
Due to the excessive cost of large-scale language model pre-training, considerable efforts have been made to train BERT progressively -- start from an inferior but low-cost model a…
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding
Jiaming Shen, Heng Ji, Jiawei Han
Linguistic steganography studies how to hide secret messages in natural language cover texts. Traditional methods aim to transform a secret message into an innocent text via lexica…
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning
Yuning Mao, Yanru Qu, Yiqing Xie +2
While neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (M…
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery
Jiaming Shen, Wenda Qiu, Jingbo Shang +3
Entity set expansion and synonym discovery are two critical NLP tasks. Previous studies accomplish them separately, without exploring their interdependencies. In this work, we hypo…
Generation-Augmented Retrieval for Open-domain Question Answering
Yuning Mao, Pengcheng He, Xiaodong Liu +4
We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts wi…
Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
Yu Meng, Yunyi Zhang, Jiaxin Huang +3
Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora. To account for potential hier…
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang, Manling Li, Xuan Wang +24
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and rel…
Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
Yuning Mao, Tong Zhao, Andrey Kan +4
Taxonomies have found wide applications in various domains, especially online for item categorization, browsing, and search. Despite the prevalent use of online catalog taxonomies,…
Open-Domain Question Answering with Pre-Constructed Question Spaces
Jinfeng Xiao, Lidan Wang, Franck Dernoncourt +3
Open-domain question answering aims at solving the task of locating the answers to user-generated questions in massive collections of documents. There are two families of solutions…
Minimally Supervised Categorization of Text with Metadata
Yu Zhang, Yu Meng, Jiaxin Huang +3
Document categorization, which aims to assign a topic label to each document, plays a fundamental role in a wide variety of applications. Despite the success of existing studies in…
Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
Xuan Wang, Xiangchen Song, Bangzheng Li +2
We created this CORD-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020-03-13). This CORD-NER data…
Empower Entity Set Expansion via Language Model Probing
Yunyi Zhang, Jiaming Shen, Jingbo Shang +1
Entity set expansion, aiming at expanding a small seed entity set with new entities belonging to the same semantic class, is a critical task that benefits many downstream NLP and I…
Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion
Jiaxin Huang, Yiqing Xie, Yu Meng +3
Given a small set of seed entities (e.g., ``USA'', ``Russia''), corpus-based set expansion is to induce an extensive set of entities which share the same semantic class (Country in…
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
Jiaming Shen, Zhihong Shen, Chenyan Xiong +3
Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies…
Generating Representative Headlines for News Stories
Xiaotao Gu, Yuning Mao, Jiawei Han +7
Millions of news articles are published online every day, which can be overwhelming for readers to follow. Grouping articles that are reporting the same event into news stories is…