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ISSN: 2666-6510

Is Chinese Spelling Check ready? Understanding the correction behavior in real-world scenarios

The task of Chinese Spelling Check (CSC) is crucial for identifying and rectifying spelling errors in Chinese texts. While prior work in this domain has predominantly relied on benchmarks such as SIGHAN...

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Word sense induction with agglomerative clustering and mutual information maximization

Word sense induction (WSI) is a challenging problem in natural language processing that involves the unsupervised automatic detection of a word’s senses (i.e., meanings). Recent work achieves significant...

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Adaptive negative representations for graph contrastive learning

Graph contrastive learning (GCL) has emerged as a promising paradigm for learning graph representations. Recently, the idea of hard negatives is introduced to GCL, which can provide more challenging...

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GPT understands, too

Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU). However, our preliminary study reveals that manual discrete prompts...

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The road from MLE to EM to VAE: A brief tutorial

Variational Auto-Encoders (VAEs) have emerged as one of the most popular genres of generative models, which are learned to characterize the data distribution. The classic Expectation Maximization (EM)...

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Human motion modeling with deep learning: A survey

The aim of human motion modeling is to understand human behaviors and create reasonable human motion like real people given different priors. With the development of deep learning, researchers tend...

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Survey: Transformer based video-language pre-training

Inspired by the success of transformer-based pre-training methods on natural language tasks and further computer vision tasks, researchers have started to apply transformer to video processing. This...

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Learning towards conversational AI: A survey

Recent years have witnessed a surge of interest in the field of open-domain dialogue. Thanks to the rapid development of social media, large dialogue corpus from the Internet builds up a fundamental...

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Data augmentation approaches in natural language processing: A survey

As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language...

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A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources

As an important way to alleviate information overload, a recommender system aims to filter out irrelevant information for users and provides them items that they may be interested in. In recent years,...

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Self-directed machine learning

Conventional machine learning (ML) relies heavily on manual design from machine learning experts to decide learning tasks, data, models, optimization algorithms, and evaluation metrics, which is labor-intensive,...

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Hierarchical label with imbalance and attributed network structure fusion for network embedding

Network embedding (NE) aims to learn low-dimensional vectors for nodes while preserving the network’s essential properties (e.g., attributes and structure). Previous methods have been proposed to learn...

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A survey of transformers

Transformers have achieved great success in many artificial intelligence fields, such as natural language processing, computer vision, and audio processing. Therefore, it is natural to attract lots...

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Optimized separable convolution: Yet another efficient convolution operator

The convolution operation is the most critical component in recent surge of deep learning research. Conventional 2D convolution needs O(C2K2) parameters to represent, where C is the channel size and...

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Deep learning for fake news detection: A comprehensive survey

The information age enables people to obtain news online through various channels, yet in the meanwhile making false news spread at unprecedented speed. Fake news exerts detrimental effects for it impairs...

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HSSDA: Hierarchical relation aided Semi-Supervised Domain Adaptation

The mainstream domain adaptation (DA) methods transfer the supervised source domain knowledge to the unsupervised or semi-supervised target domain, so as to assist the classification task in the target...

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BCA: Bilinear Convolutional Neural Networks and Attention Networks for legal question answering

The National Judicial Examination of China is an essential examination for selecting legal practitioners. In recent years, people have tried to use machine learning algorithms to answer examination...

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PTR: Prompt Tuning with Rules for Text Classification

Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved promising results on some...

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Debiased recommendation with neural stratification

Debiased recommender models have recently attracted increasing attention from the academic and industry communities. Existing models are mostly based on the technique of inverse propensity score (IPS)....

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CAILIE 1.0: A dataset for Challenge of AI in Law - Information Extraction V1.0

Legal information extraction requires identifying and classifying legal elements from specific legal documents. Considering that information extraction is mainly regarded as the first step in natural...

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Domain generalization by class-aware negative sampling-based contrastive learning

When faced with the issue of different feature distribution between training and test data, the test data may differ in style and background from the training data due to the collection sources or privacy...

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On the distribution alignment of propagation in graph neural networks

Graph neural networks (GNNs) have been widely adopted for modeling graph-structure data. Most existing GNN studies have focused on designing different strategies to propagate information over the graph...

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