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Found 8 papers, 7 papers with code
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
The rise of deep learning in today's applications entailed an increasing need in explaining the model's decisions beyond prediction performances in order to foster trust and accountability.
Explaining and Interpreting LSTMs
While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the variety of tasks to be solved.
Discovering topics in text datasets by visualizing relevant words
When dealing with large collections of documents, it is imperative to quickly get an overview of the texts' contents.
Exploring text datasets by visualizing relevant words
When working with a new dataset, it is important to first explore and familiarize oneself with it, before applying any advanced machine learning algorithms.
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions.
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment.
Explaining Predictions of Non-Linear Classifiers in NLP
Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables.
