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Recurrent Neural Network for Text Classification with Multi-Task Learning
This paper uses the multi-task learning framework to jointly learn across multiple related tasks based on recurrent neural network to propose three different mechanisms of sharing information to model text with task-specific and shared layers.
Adversarial Multi-task Learning for Text Classification
This paper proposes an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other, and shows that the shared knowledge learned can be regarded as off-the-shelf knowledge and easily transferred to new tasks.
Long Short-Term Memory Neural Networks for Chinese Word Segmentation
- Xinchi Chen, Xipeng Qiu, Chenxi Zhu, Pengfei Liu, Xuanjing Huang
- Computer ScienceEMNLP
- 1 September 2015
A novel neural network model for Chinese word segmentation is proposed, which adopts the long short-term memory (LSTM) neural network to keep the previous important information in memory cell and avoids the limit of window size of local context.
How to Fine-Tune BERT for Text Classification?
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.
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
K-Adapter is proposed, which remains the original parameters of the pre-trained model fixed and supports continual knowledge infusion and captures richer factual and commonsense knowledge than RoBERTa.
Gated Recursive Neural Network for Chinese Word Segmentation
A gated recursive neural network (GRNN) for Chinese word segmentation is proposed, which contains reset and update gates to incorporate the complicated combinations of the context characters.
Pre-trained Models for Natural Language Processing: A Survey
- Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, Xuanjing Huang
- Computer ScienceArXiv
- 18 March 2020
This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
This paper construct context-gloss pairs and propose three BERT based models for WSD and fine-tune the pre-trained BERT model to achieve new state-of-the-art results on WSD task.
Convolutional Neural Tensor Network Architecture for Community-Based Question Answering
This paper proposes a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer, which outperforms the other methods on two matching tasks.
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
The Style Transformer is proposed, which makes no assumption about the latent representation of source sentence and equips the power of attention mechanism in Transformer to achieve better style transfer and better content preservation.