| Challenge: | Many natural language processing tasks can be formulated as deep reinforcement learning (DRL) problems. |
| Approach: | This tutorial provides an introduction to the foundations of deep reinforcement learning . it describes recent advances in designing deep reinforcement for NLP . |
| Outcome: | This tutorial provides an introduction to the foundations of deep reinforcement learning and some practical solutions for NLP tasks. |
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| 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. |
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Deep Adversarial Learning for NLP (N19-5)
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| Challenge: | Adversarial learning is a game-theoretic learning paradigm that has achieved huge successes in the field of Computer Vision recently. |
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Deep Bayesian Natural Language Processing (P19-4)
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| Challenge: | Introduction to deep Bayesian learning for natural language addresses the fundamentals of statistical models and neural networks. |
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Efficient Methods for Natural Language Processing: A Survey (2023.tacl-1)
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Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz
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Dive into Deep Learning for Natural Language Processing (D19-2)
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| Challenge: | GluonNLP is a powerful new toolkit that automates the most laborious aspects of deep learning for NLP. |
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Deep Learning Approaches to Text Production (N18-6)
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| Challenge: | Text production is a key component of many NLP applications . Claire Gardent is based in France and is pursuing research in text production . |
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| Challenge: | COLING 2018 is a conference for researchers and practitioners working on machine learning and deep learning. |
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| Challenge: | Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation systems. |
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Discreteness in Neural Natural Language Processing (D19-2)
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| Challenge: | This tutorial provides a comprehensive guide to the process of discreteness in neural NLP. |
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