1k citations · 2.7k across the 61 of their papers we have counts for
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Multi-task Learning over Graph Structures
Pengfei Liu, Jie Fu, Yue Dong +2
We present two architectures for multi-task learning with neural sequence models. Our approach allows the relationships between different tasks to be learned dynamically, rather th…
Contextualized Non-local Neural Networks for Sequence Learning
Pengfei Liu, Shuaichen Chang, Xuanjing Huang +2
Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which self-attention, as exemplified by the Transformer model, and graph neura…
Meta-Learning Multi-task Communication
Pengfei Liu, Xuanjing Huang
In this paper, we describe a general framework: Parameters Read-Write Networks (PRaWNs) to systematically analyze current neural models for multi-task learning, in which we find th…
Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network
Pengfei Liu, Ji Zhang, Cane Wing-Ki Leung +2
Effective representation of a text is critical for various natural language processing tasks. For the particular task of Chinese sentiment analysis, it is important to understand a…
Meta Multi-Task Learning for Sequence Modeling
Junkun Chen, Xipeng Qiu, Pengfei Liu +1
Semantic composition functions have been playing a pivotal role in neural representation learning of text sequences. In spite of their success, most existing models suffer from the…