Representation Learning for Natural Language Processing
arXiv:2102.03732 · doi:10.1007/978-981-15-5573-2
Abstract
This book aims to review and present the recent advances of distributed representation learning for NLP, including why representation learning can improve NLP, how representation learning takes part in various important topics of NLP, and what challenges are still not well addressed by distributed representation.
Published in Springer
References in corpus (80)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Efficient Estimation of Word Representations in Vector Space
- Neural Machine Translation by Jointly Learning to Align and Translate
- Sequence to Sequence Learning with Neural Networks
- Modularity and community structure in networks
- DeepWalk: Online Learning of Social Representations
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Distributed Representations of Sentences and Documents
- Finding community structure in networks using the eigenvectors of matrices
- LINE: Large-scale Information Network Embedding
- A Survey on Knowledge Graphs: Representation, Acquisition and Applications
- Spectral Networks and Locally Connected Networks on Graphs
- Graph Embedding Techniques, Applications, and Performance: A Survey
- Molecular Graph Convolutions: Moving Beyond Fingerprints
- A Review of Relational Machine Learning for Knowledge Graphs
- ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
- Cross-lingual Language Model Pretraining
- Bidirectional Attention Flow for Machine Comprehension
- Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition
- Exploiting Similarities among Languages for Machine Translation
- Deep Convolutional Networks on Graph-Structured Data
- Supervised Topic Models
- Hierarchical Question-Image Co-Attention for Visual Question Answering
- Complex Embeddings for Simple Link Prediction
- Language Modeling with Gated Convolutional Networks
- Graph Convolutional Matrix Completion
- Learning Combinatorial Optimization Algorithms over Graphs
- Recurrent Neural Network for Text Classification with Multi-Task Learning
- Representation Learning on Graphs with Jumping Knowledge Networks
- Unified Language Model Pre-training for Natural Language Understanding and Generation
- A Deep Relevance Matching Model for Ad-hoc Retrieval
- Zero-Shot Learning Through Cross-Modal Transfer
- Zero-Shot Learning by Convex Combination of Semantic Embeddings
- PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
- Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
- End-to-End Neural Ad-hoc Ranking with Kernel Pooling
- Large-scale Simple Question Answering with Memory Networks
- Highway Networks
- Discriminative Embeddings of Latent Variable Models for Structured Data
- Diffusion-Convolutional Neural Networks
- On Smoothing and Inference for Topic Models
- Learning Deep Generative Models of Graphs
- Gated Feedback Recurrent Neural Networks
- Exploring Models and Data for Image Question Answering
- A Fast and Simple Algorithm for Training Neural Probabilistic Language Models
- Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
- Reducing Transformer Depth on Demand with Structured Dropout
- Hierarchical relational models for document networks
- Neural Relational Inference for Interacting Systems
- SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
- A Comprehensive Survey on Cross-modal Retrieval
- Experimental Support for a Categorical Compositional Distributional Model of Meaning
- Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
- DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval
- NetGAN: Generating Graphs via Random Walks
- Syntactic Topic Models
- Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
- Cross-Sentence N-ary Relation Extraction with Graph LSTMs
- A Survey of Word Embeddings Evaluation Methods
- Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions
- TransA: An Adaptive Approach for Knowledge Graph Embedding
- Word-Entity Duet Representations for Document Ranking
- A Neural Knowledge Language Model
- Traversing Knowledge Graphs in Vector Space
- Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
- Contrastive Learning for Image Captioning
- TransG : A Generative Mixture Model for Knowledge Graph Embedding
- Probabilistic Reasoning via Deep Learning: Neural Association Models
- Document Context Language Models
- Network Representation Learning: A Survey
- How to Generate a Good Word Embedding?
- Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion
- Image Captioning with Deep Bidirectional LSTMs
- WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation
- Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
- Knowledge Graph Representation with Jointly Structural and Textual Encoding
- Cross-modal Common Representation Learning by Hybrid Transfer Network
- An Attentive Neural Architecture for Fine-grained Entity Type Classification
- Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge
- Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition