| Challenge: | Adversarial learning is a game-theoretic learning paradigm that has achieved huge successes in the field of Computer Vision recently. |
| Approach: | This tutorial introduces the foundations of deep adversarial learning and some practical problems and solutions in NLP. |
| Outcome: | This tutorial introduces the foundations of deep adversarial learning and some practical problems and solutions in NLP. |
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Deep Reinforcement Learning for NLP (P18-5)
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| 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. |
Deep Learning for Natural Language Inference (N19-5)
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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. |
| 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. |
Adversarial NLI: A New Benchmark for Natural Language Understanding (2020.acl-main)
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| Challenge: | a new large-scale NLI benchmark dataset is presented to test models on a variety of popular NLIs. |
| Approach: | They propose a large-scale NLI benchmark dataset that is iteratively compared with a human-and-model-in-the-loop procedure. |
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Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | This tutorial aims to raise awareness of practical concerns about NLP robustness . it aims at addressing the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
| Approach: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it reviews recent studies on analyzing the weakness of NLP systems when facing adversarial inputs . |
| Outcome: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it will examine the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
Diverse Adversaries for Mitigating Bias in Training (2021.eacl-main)
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| Challenge: | Existing adversarial methods only partially mitigate the problem of model bias, added to which their training procedures are unstable. |
| Approach: | They propose a method where discriminators are encouraged to learn orthogonal hidden representations from one another to reduce model bias. |
| Outcome: | The proposed method significantly reduces bias and stability of training over standard methods. |
Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
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| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
| Approach: | This tutorial introduces contrastive learning and provides an introduction to the techniques. |
| Outcome: | This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them. |
Beyond Paragraphs: NLP for Long Sequences (2021.naacl-tutorials)
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| Challenge: | In this tutorial, we will introduce document-level representation learning techniques . document-based learning is challenging due to the limited sequence length of many models . |
| Approach: | They will provide an overview of established long sequence NLP techniques and discuss memory-saving methods that are key to processing long sequences. |
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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. |
| 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 . |
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Towards Improving Adversarial Training of NLP Models (2021.findings-emnlp)
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| Challenge: | Recent methods for generating NLP adversarial examples involve combinatorial search and expensive sentence encoders for constraining the generated instances. |
| Approach: | They propose to use vanilla adversarial training to train NLP models using a word substitution attack optimized for vanilla adversary training. |
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A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing (2023.eacl-main)
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| Challenge: | Developing methods to improve model performance in imbalanced data settings has been an active area for decades . |
| Approach: | They propose to use sampling, data augmentation, choice of loss function, staged learning, or model design to address class imbalance in NLP. |
| Outcome: | The proposed approaches are evaluated on a variety of NLP tasks or in the computer vision community. |