| Challenge: | Word embeddings lack interpretability, but rotation of word spaces can help . e.g., lexicon induction, gender bias can be removed by removing interpretable dimensions . |
| Approach: | They propose three methods for making word embeddings interpretable by rotation . they use Densifier, linear SVMs and DensRay to compute word spaces in closed form . |
| Outcome: | The proposed method can be computed in closed form and is more robust than Densifier. |
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SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings (2022.findings-emnlp)
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| Challenge: | Existing word embedding models lack interpretability for words . |
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Word2Sense: Sparse Interpretable Word Embeddings (P19-1)
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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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The Word Analogy Testing Caveat (N18-2)
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Embeddings in Natural Language Processing (2020.coling-tutorials)
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Which Evaluations Uncover Sense Representations that Actually Make Sense? (2020.lrec-1)
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