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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…
Long Short-Term Memory with Dynamic Skip Connections
Tao Gui, Qi Zhang, Lujun Zhao +4
In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing lo…
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…
Deformable Stacked Structure for Named Entity Recognition
Shuyang Cao, Xipeng Qiu, Xuanjing Huang
Neural architecture for named entity recognition has achieved great success in the field of natural language processing. Currently, the dominating architecture consists of a bi-dir…
Gaussian Word Embedding with a Wasserstein Distance Loss
Chi Sun, Hang Yan, Xipeng Qiu +1
Compared with word embedding based on point representation, distribution-based word embedding shows more flexibility in expressing uncertainty and therefore embeds richer semantic…
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks
Junkun Chen, Kaiyu Chen, Xinchi Chen +2
Designing shared neural architecture plays an important role in multi-task learning. The challenge is that finding an optimal sharing scheme heavily relies on the expert knowledge…
Information Aggregation via Dynamic Routing for Sequence Encoding
Jingjing Gong, Xipeng Qiu, Shaojing Wang +1
While much progress has been made in how to encode a text sequence into a sequence of vectors, less attention has been paid to how to aggregate these preceding vectors (outputs of…
Toward Diverse Text Generation with Inverse Reinforcement Learning
Zhan Shi, Xinchi Chen, Xipeng Qiu +1
Text generation is a crucial task in NLP. Recently, several adversarial generative models have been proposed to improve the exposure bias problem in text generation. Though these m…
Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method
Jinyue Su, Jiacheng Xu, Xipeng Qiu +1
Generating plausible and fluent sentence with desired properties has long been a challenge. Most of the recent works use recurrent neural networks (RNNs) and their variants to pred…
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…