Graph neural networks: A review of methods and applications
2020
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface,...
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Pre-trained models: Past, present and future
2021
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophisticated pre-training...
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Neural machine translation: A review of methods, resources, and tools
2020
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machine translation (NMT)...
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Survey: Transformer based video-language pre-training
2022
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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Advances and challenges in conversational recommender systems: A survey
2021
Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to...
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Extracting Events and Their Relations from Texts: A Survey on Recent Research Progress and Challenges
2020
Event is a common but non-negligible knowledge type. How to identify events from texts, extract their arguments, even analyze the relations between different events are important for many applications....
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A comprehensive survey of entity alignment for knowledge graphs
2021
Knowledge Graphs (KGs), as a structured human knowledge, manage data in an ease-of-store, recognizable, and understandable way for machines and provide a rich knowledge base for different artificial...
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Neural, symbolic and neural-symbolic reasoning on knowledge graphs
2021
Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval, and recommendation. Since knowledge graphs can...
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Learning towards conversational AI: A survey
2022
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
Available online 24 March 2022
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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The road from MLE to EM to VAE: A brief tutorial
2022
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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Discrete and continuous representations and processing in deep learning: Looking forward
2021
Discrete and continuous representations of content (e.g., of language or images) have interesting properties to be explored for the understanding of or reasoning with this content by machines. This...
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CPM: A large-scale generative Chinese Pre-trained language model
2021
Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570 GB training data, drew a lot of attention due to...
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A comprehensive review on resolving ambiguities in natural language processing
2021
Natural language processing is a known technology behind the development of some widely known AI assistants such as: SIRI, Natasha, and Watson. However, NLP is a diverse technology used for numerous...
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CPM-2: Large-scale cost-effective pre-trained language models
2021
In recent years, the size of pre-trained language models (PLMs) has grown by leaps and bounds. However, efficiency issues of these large-scale PLMs limit their utilization in real-world scenarios. We...
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Human motion modeling with deep learning: A survey
2022
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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Robustness of deep learning models on graphs: A survey
2021
Machine learning (ML) technologies have achieved significant success in various downstream tasks, e.g., node classification, link prediction, community detection, graph classification and graph clustering....
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Lawformer: A pre-trained language model for Chinese legal long documents
2021
Legal artificial intelligence (LegalAI) aims to benefit legal systems with the technology of artificial intelligence, especially natural language processing (NLP). Recently, inspired by the success...
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WuDaoCorpora: A super large-scale Chinese corpora for pre-training language models
2021
Using large-scale training data to build a pre-trained language model (PLM) with a larger volume of parameters can significantly improve downstream tasks. For example, OpenAI trained the GPT3 model...
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A review of deep learning in question answering over knowledge bases
2021
Question answering over knowledge bases (KBQA) is a challenging task in natural language processing. It requires machines to answer natural language questions based on large-scale knowledge bases. Recent...
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A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources
Available online 2 April 2022
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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AI-driven drug discovery: A boon against COVID-19?
2020
The COVID-19 is an issue of international concern and threat to public health and there is an urgent need of drug/vaccine design. There is no vaccine or specific drug yet made as of July 23, 2020, for...
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Know what you don't need: Single-Shot Meta-Pruning for attention heads
2021
Deep pre-trained Transformer models have achieved state-of-the-art results over a variety of natural language processing (NLP) tasks. By learning rich language knowledge with millions of parameters,...
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CokeBERT: Contextual knowledge selection and embedding towards enhanced pre-trained language models
2021
Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs), and achieved consistent improvements on...
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Rule-based data augmentation for knowledge graph embedding
2021
Knowledge graph (KG) embedding models suffer from the incompleteness issue of observed facts. Different from existing solutions that incorporate additional information or employ expressive and complex...
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