Categorization
We follow the ACL and ICML submission guideline of recent years, covering a broad range of areas in NLP and ML. The categorization is as follows:
Natural Language Processing
Computational Social Science and Social Media
Dialogue and Interactive Systems
Generation
Information Extraction
Information Retrieval and Text Mining
Interpretability and Analysis of Models for NLP
Knowledge Graph
Language Grounding to Vision, Robotics and Beyond
Linguistic Theories, Cognitive Modeling and Psycholinguistics
Machine Learning for NLP
Machine Translation
Natural Language Processing
Named Entity Recognition (NER)
NLP Applications
Question Answering
Reading Comprehension
Recommender Systems
Resources and Evaluation
Semantics
Sentiment Analysis, Stylistic Analysis, and Argument Mining
Speech and Multimodality
Summarization
Syntax: Tagging, Chunking, Syntax and Parsing
Text Classification
Machine Learning
Architectures
AutoML
Bayesian Methods
Classification,Clustering,Regression
Curriculum Learning
Data Augmentation
Deep Learning - General Methods
Deep Reinforcement Learning
Federated Learning
Few-Shot and Zero-Shot Learning
General Machine Learning
Generative Adversarial Networks
Graph Neural Networks
Interpretability and Analysis
Meta Learning
Metric Learning
ML Applications
Model Compression and Acceleration
Multi-Task and Multi-View Learning
Online Learning
Optimization
Semi-Supervised and Unsupervised Learning
Transfer Learning
Trustworthy Machine Learning