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Xipeng Qiu
Researcher at Fudan University
Publications - 278
Citations - 11673
Xipeng Qiu is an academic researcher from Fudan University. The author has contributed to research in topics: Computer science & Automatic summarization. The author has an hindex of 43, co-authored 227 publications receiving 7402 citations. Previous affiliations of Xipeng Qiu include New York University & Carnegie Mellon University.
Papers
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Journal ArticleDOI
Pre-trained Models for Natural Language Processing: A Survey
TL;DR: Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era as mentioned in this paper, and a comprehensive review of PTMs for NLP can be found in this survey.
Book ChapterDOI
How to Fine-Tune BERT for Text Classification?
TL;DR: A general solution for BERT fine-tuning is provided and new state-of-the-art results on eight widely-studied text classification datasets are obtained.
Proceedings Article
Recurrent neural network for text classification with multi-task learning
TL;DR: This article proposed three different mechanisms of sharing information to model text with task-specific and shared layers, which can improve the performance of a task with the help of other related tasks in text classification.
Proceedings ArticleDOI
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
Chi Sun,Luyao Huang,Xipeng Qiu +2 more
TL;DR: This paper constructs an auxiliary sentence from the aspect and converts ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI), and fine-tune the pre-trained model from BERT.
Proceedings ArticleDOI
Adversarial Multi-task Learning for Text Classification
TL;DR: This paper proposed an adversarial multi-task learning framework, which alleviates the shared and private latent feature spaces from interfering with each other and showed that the shared knowledge learned by their proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks.