| Challenge: | COLING 2018 is a conference for researchers and practitioners working on machine learning and deep learning. |
| Approach: | a tutorial on machine learning and deep learning will be presented at COLING 2018 . the tutorial will focus on statistical models, deep neural networks, sequential learning and natural language understanding . |
| Outcome: | This tutorial will present the latest advances in deep Bayesian and sequential learning at COLING 2018 . |
Similar Papers
Deep Bayesian Natural Language Processing (P19-4)
Copied to clipboard
| Challenge: | Introduction to deep Bayesian learning for natural language addresses the fundamentals of statistical models and neural networks. |
| Approach: | This tutorial addresses the advances in deep Bayesian learning for natural language . it focuses on advanced Bayessian models and deep models . authors present case studies and domain applications to tackle different issues . |
| Outcome: | This tutorial focuses on advanced Bayesian models and deep models for natural language . case studies and domain applications are presented to tackle different issues in deep Bayessian processing, learning and understanding. |
Deep Learning for Natural Language Inference (N19-5)
Copied to clipboard
| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
| Outcome: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning model for language understanding and reasoning. |
Deep Latent Variable Models of Natural Language (D18-3)
Copied to clipboard
| Challenge: | In this tutorial, we will discuss the challenges of applying neural variational inference to NLP problems. |
| Approach: | The tutorial will cover deep latent variable models in the case where exact inference over the latent variables is tractable. |
| Outcome: | The proposed tutorial will cover deep latent variable models in the case where inference cannot be performed tractably and when it is not . |
CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing (2022.emnlp-tutorials)
Copied to clipboard
| Challenge: | Establishing causal relationships is a fundamental goal of scientific research . lack of clear definitions, notations, benchmark datasets, and challenges remains . |
| Approach: | They introduce the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provide an overview of causal perspectives to NLP problems. |
| Outcome: | This tutorial introduces the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provides an overview of causal perspectives to NLP problems. |
Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts (C18-3)
Copied to clipboard
| Challenge: | COLING 2018 is a three-hour tutorial series covering a range of core problems and exciting developments in computational linguistics and natural language processing. |
| Approach: | COLING 2018 has six tutorials covering a range of core problems and exciting developments in computational linguistics and natural language processing. |
| Outcome: | COLING 2018 will host six tutorials covering a range of core problems and exciting developments in computational linguistics and natural language processing. |
Findings of the Association for Computational Linguistics: NAACL 2022 (2022.findings-naacl)
Copied to clipboard
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) (D19-61)
Copied to clipboard
| Challenge: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . |
| Approach: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . call for papers for this second workshop met with a strong response . |
| Outcome: | the EMNLP-IJCNLP 2019 workshop on deep learning approaches for low-resource natural language processing takes place in Hong Kong, China. |
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
Findings of the Association for Computational Linguistics: ACL 2024 (2024.findings-acl)
Copied to clipboard
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021.findings-acl)
Copied to clipboard
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |