Textual Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis (P18-1)
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| Challenge: | Existing approaches to text analysis make no assumptions about linguistic structure and focus on stastically frequent patterns. |
| Approach: | They propose a new strategy to visualize linguistic information detected by a CNN for text classification. |
| Outcome: | The proposed strategy automatically encodes complex linguistic patterns on three different languages for each dataset. |
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Interpretable Text Embeddings and Text Similarity Explanation: A Survey (2025.emnlp-main)
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
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Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference (D18-1)
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Deep Bayesian Natural Language Processing (P19-4)
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Why Generate When You Can Discriminate? A Novel Technique for Text Classification using Language Models (2024.findings-eacl)
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Evaluating Saliency Methods for Neural Language Models (2021.naacl-main)
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| Challenge: | a general complaint of neural network models is that their internal decision mechanisms are hard to understand. |
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Human-grounded Evaluations of Explanation Methods for Text Classification (D19-1)
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| Challenge: | Explainable Artificial Intelligence (XAI) is aimed at providing explanations for decisions made by AI systems. |
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Deep Learning Approaches to Text Production (N18-6)
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Adaptive Convolution for Text Classification (N19-1)
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