| Challenge: | Existing work on deep neural networks has focused on representation analysis, but recent work focused on analyzing neurons within these models. |
| Approach: | They propose to analyze neural networks to uncover linguistic concepts captured by the network . they propose to use a granular approach to analyze neurons within these models . |
| Outcome: | The proposed method combines methods to discover and understand neurons in a network with evaluation methods. |
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| Challenge: | a tutorial aims to introduce the nascent field of interpretability and analysis of neural networks in NLP . |
| Approach: | This tutorial will introduce the nascent field of interpretability and analysis of neural networks in NLP. |
| Outcome: | This tutorial will introduce the nascent field of interpretability and analysis of neural networks in NLP. |
Interpreting Predictions of NLP Models (2020.emnlp-tutorials)
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| Challenge: | This tutorial will provide a background on interpretation techniques for neural NLP models. |
| Approach: | This tutorial will provide a background on interpretation techniques for NLP models . it will examine saliency maps, input perturbations, adversarial attacks and influence functions . |
| Outcome: | This tutorial will provide a background on interpretation techniques . examples-specific interpretations include saliency maps, input perturbations, adversarial attacks, influence functions . |
Fine-grained Interpretation and Causation Analysis in Deep NLP Models (2021.naacl-tutorials)
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| Challenge: | Despite the proven efficacy of deep neural networks at-large, their opaqueness is a major cause of concern. |
| Approach: | They will present research work on interpreting fine-grained components of a neural network model from two perspectives, i) fine-grain interpretation, and ii) causation analysis. |
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Deep Neural Model Inspection and Comparison via Functional Neuron Pathways (P19-1)
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| Challenge: | a general method for the interpretation and comparison of neural models is proposed . we factor a complex neural model into its functional components . |
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Analyzing Individual Neurons in Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Recent work shows that deep NLP models capture linguistic knowledge but little attention is paid to individual neurons. |
| Approach: | They conduct a neuron-level analysis of pre-trained neural language models to determine linguistic properties. |
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SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)
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| Challenge: | Existing models with opacity problems have been proposed to address this problem. |
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A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
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| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
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Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? (2020.acl-main)
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Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement (P18-1)
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| Challenge: | a number of post hoc explanation methods for deep neural networks have been proposed . due to the complexity of the DNNs they explain, these methods are necessarily approximations and come with their own sources of error. |
| Approach: | They propose two evaluation paradigms that cover two important classes of NLP problems . they propose LIMSSE, LRP and DeepLIFT as the most effective explanation methods . |
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Neural Unsupervised Domain Adaptation in NLP—A Survey (2020.coling-main)
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| Challenge: | Deep neural networks excel at learning from labeled data, but learning from unlabeled data remains a challenge. |
| Approach: | They review neural unsupervised domain adaptation techniques which do not require labeled target domain data. |
| Outcome: | The proposed techniques are more challenging yet widely applicable. |