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Found 8 papers, 8 papers with code
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers
Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain.
Neural Vector Conceptualization for Word Vector Space Interpretation
Distributed word vector spaces are considered hard to interpret which hinders the understanding of natural language processing (NLP) models.
Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling
Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions.
Multilingual Relation Classification via Efficient and Effective Prompting
Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes.
Learning Explanations from Language Data
PatternAttribution is a recent method, introduced in the vision domain, that explains classifications of deep neural networks.
Train, Sort, Explain: Learning to Diagnose Translation Models
Evaluating translation models is a trade-off between effort and detail.
Why only Micro-F1? Class Weighting of Measures for Relation Classification
Relation classification models are conventionally evaluated using only a single measure, e. g., micro-F1, macro-F1 or AUC.
Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling
We present a novel explanation method, called OLM, for natural language processing classifiers.
