research
∙
12/19/2022
Transferring General Multimodal Pretrained Models to Text Recognition
This paper proposes a new method, OFA-OCR, to transfer multimodal pretra...
share
research
∙
12/08/2022
OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models
Generalist models, which are capable of performing diverse multi-modal t...
research
∙
10/28/2022
DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention
Vision-and-language (V-L) tasks require the system to understand both vi...
research
∙
10/19/2022
Prophet Attention: Predicting Attention with Future Attention for Improved Image Captioning
Recently, attention based models have been used extensively in many sequ...
research
∙
10/11/2022
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models
Investigating better ways to reuse the released pre-trained language mod...
research
∙
06/04/2022
Rethinking the Openness of CLIP
Contrastive Language-Image Pre-training (CLIP) has demonstrated great po...
research
∙
03/20/2022
Hierarchical Inductive Transfer for Continual Dialogue Learning
Pre-trained models have achieved excellent performance on the dialogue t...
research
∙
12/14/2021
Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models
As many fine-tuned pre-trained language models (PLMs) with promising per...
research
∙
10/13/2021
Well-classified Examples are Underestimated in Classification with Deep Neural Networks
The conventional wisdom behind learning deep classification models is to...
research
∙
10/08/2021
Topology-Imbalance Learning for Semi-Supervised Node Classification
The class imbalance problem, as an important issue in learning node repr...
research
∙
09/07/2021
Adversarial Parameter Defense by Multi-Step Risk Minimization
Previous studies demonstrate DNNs' vulnerability to adversarial examples...
research
∙
08/05/2021
O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning
Video captioning combines video understanding and language generation. D...
research
∙
05/15/2021
Rethinking Skip Connection with Layer Normalization in Transformers and ResNets
Skip connection, is a widely-used technique to improve the performance a...
research
∙
03/29/2021
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models
Recent studies have revealed a security threat to natural language proce...
research
∙
02/22/2021
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
Neural dialogue models suffer from low-quality responses when interacted...
research
∙
12/29/2020
Accelerating Pre-trained Language Models via Calibrated Cascade
Dynamic early exiting aims to accelerate pre-trained language models' (P...
research
∙
10/13/2020
CAPT: Contrastive Pre-Training for LearningDenoised Sequence Representations
Pre-trained self-supervised models such as BERT have achieved striking s...
research
∙
10/05/2020
Regularizing Dialogue Generation by Imitating Implicit Scenarios
Human dialogues are scenario-based and appropriate responses generally r...
research
∙
09/16/2020
Collaborative Group Learning
Collaborative learning has successfully applied knowledge transfer to gu...
research
∙
06/10/2020
Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption
We argue that the vulnerability of model parameters is of crucial value ...
research
∙
05/16/2020
Layer-Wise Cross-View Decoding for Sequence-to-Sequence Learning
In sequence-to-sequence learning, the attention mechanism has been a gre...
research
∙
02/28/2020
Exploring and Distilling Cross-Modal Information for Image Captioning
Recently, attention-based encoder-decoder models have been used extensiv...
research
∙
12/25/2019
Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
Self-attention based Transformer has demonstrated the state-of-the-art p...
research
∙
10/27/2019
An Adaptive and Momental Bound Method for Stochastic Learning
Training deep neural networks requires intricate initialization and care...
research
∙
10/11/2019
Incorporating Fine-grained Events in Stock Movement Prediction
Considering event structure information has proven helpful in text-based...
research
∙
06/27/2019
PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation
Chinese word segmentation (CWS) is a fundamental step of Chinese natural...
research
∙
06/05/2019
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
Unsupervised text style transfer aims to alter text styles while preserv...
research
∙
05/24/2019
Memorized Sparse Backpropagation
Neural network learning is typically slow since backpropagation needs to...
research
∙
05/15/2019
Aligning Visual Regions and Textual Concepts: Learning Fine-Grained Image Representations for Image Captioning
In image-grounded text generation, fine-grained representations of the i...
research
∙
09/11/2018
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet
Automatic evaluation of semantic rationality is an important yet challen...
research
∙
08/27/2018
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions
The encode-decoder framework has shown recent success in image captionin...
research
∙
08/23/2018
Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations
This paper explores a new natural language processing task, review-drive...
research
∙
08/22/2018
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation
Most of the Neural Machine Translation (NMT) models are based on the seq...
research
∙
08/21/2018
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
Narrative story generation is a challenging problem because it demands t...
research
∙
08/16/2018
Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
Huge numbers of new words emerge every day, leading to a great need for ...
research
∙
06/10/2018
Deconvolution-Based Global Decoding for Neural Machine Translation
A great proportion of sequence-to-sequence (Seq2Seq) models for Neural M...
research
∙
05/14/2018
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
The goal of sentiment-to-sentiment "translation" is to change the underl...
research
∙
05/10/2018
Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency
Abstractive text summarization is a highly difficult problem, and the se...
research
∙
05/03/2018
A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification
Text summarization and sentiment classification both aim to capture the ...
research
∙
03/15/2018
Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
Relation classification is an important semantic processing task in the ...
research
∙
03/05/2018
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Most recent approaches use the sequence-to-sequence model for paraphrase...
research
∙
03/05/2018
Word Embedding Attention Network: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Most recent approaches use the sequence-to-sequence model for paraphrase...
research
∙
02/05/2018
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text
Existing text generation methods tend to produce repeated and "boring" e...
research
∙
01/20/2018
Building an Ellipsis-aware Chinese Dependency Treebank for Web Text
Web 2.0 has brought with it numerous user-produced data revealing one's ...
research
∙
11/28/2017
Hybrid Oracle: Making Use of Ambiguity in Transition-based Chinese Dependency Parsing
In the training of transition-based dependency parsers, an oracle is use...
research
∙
11/25/2017
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
Recent systems on structured prediction focus on increasing the level of...
research
∙
11/17/2017
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method
We propose a simple yet effective technique to simplify the training and...
research
∙
10/28/2017
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
We propose a method, called Label Embedding Network, which can learn lab...
research
∙
09/18/2017
Minimal Effort Back Propagation for Convolutional Neural Networks
As traditional neural network consumes a significant amount of computing...
research
∙
06/19/2017
