127 citations · 227 across the 14 of their papers we have counts for
8 papers · 1 filter
GLM-OCR Technical Report
Shuaiqi Duan, Yadong Xue, Weihan Wang +20
GLM-OCR is an efficient 0.9B-parameter compact multimodal model designed for real-world document understanding. It combines a 0.4B-parameter CogViT visual encoder with a 0.5B-param…
UCPhrase: Unsupervised Context-aware Quality Phrase Tagging
Xiaotao Gu, Zihan Wang, Zhenyu Bi +4
Identifying and understanding quality phrases from context is a fundamental task in text mining. The most challenging part of this task arguably lies in uncommon, emerging, and dom…
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…
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…
Learning Dynamic Context Augmentation for Global Entity Linking
Xiyuan Yang, Xiaotao Gu, Sheng Lin +6
Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption…
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering
Bo Chen, Xiaotao Gu, Yufeng Hu +4
Recently, distant supervision has gained great success on Fine-grained Entity Typing (FET). Despite its efficiency in reducing manual labeling efforts, it also brings the challenge…
Learning Named Entity Tagger using Domain-Specific Dictionary
Jingbo Shang, Liyuan Liu, Xiang Ren +3
Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts o…
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
Yuning Mao, Xiang Ren, Jiaming Shen +2
We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e.,…