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Knowledge Graph Representation with Jointly Structural and Textual Encoding
Jiacheng Xu, Kan Chen, Xipeng Qiu +1
The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focuse…
End-to-End Neural Sentence Ordering Using Pointer Network
Jingjing Gong, Xinchi Chen, Xipeng Qiu +1
Sentence ordering is one of important tasks in NLP. Previous works mainly focused on improving its performance by using pair-wise strategy. However, it is nontrivial for pair-wise…
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
Xinchi Chen, Xipeng Qiu, Xuanjing Huang
Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering. H…
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification
Jiacheng Xu, Danlu Chen, Xipeng Qiu +1
Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering. However, one of the remaining challenges is…
Deep Multi-Task Learning with Shared Memory
Pengfei Liu, Xipeng Qiu, Xuanjing Huang
Neural network based models have achieved impressive results on various specific tasks. However, in previous works, most models are learned separately based on single-task supervis…
Learning Word Embeddings from Intrinsic and Extrinsic Views
Jifan Chen, Kan Chen, Xipeng Qiu +3
While word embeddings are currently predominant for natural language processing, most of existing models learn them solely from their contexts. However, these context-based word em…
Neural Sentence Ordering
Xinchi Chen, Xipeng Qiu, Xuanjing Huang
Sentence ordering is a general and critical task for natural language generation applications. Previous works have focused on improving its performance in an external, downstream t…
Syntax-based Attention Model for Natural Language Inference
PengFei Liu, Xipeng Qiu, Xuanjing Huang
Introducing attentional mechanism in neural network is a powerful concept, and has achieved impressive results in many natural language processing tasks. However, most of the exist…
Modelling Interaction of Sentence Pair with coupled-LSTMs
Pengfei Liu, Xipeng Qiu, Xuanjing Huang
Recently, there is rising interest in modelling the interactions of two sentences with deep neural networks. However, most of the existing methods encode two sequences with separat…
Recurrent Neural Network for Text Classification with Multi-Task Learning
Pengfei Liu, Xipeng Qiu, Xuanjing Huang
Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single…
Bridging LSTM Architecture and the Neural Dynamics during Reading
Peng Qian, Xipeng Qiu, Xuanjing Huang
Recently, the long short-term memory neural network (LSTM) has attracted wide interest due to its success in many tasks. LSTM architecture consists of a memory cell and three gates…