Marzia Nouri
2024
Latent Concept-based Explanation of NLP Models
Xuemin Yu
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Fahim Dalvi
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Nadir Durrani
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Marzia Nouri
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Hassan Sajjad
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Interpreting and understanding the predictions made by deep learning models poses a formidable challenge due to their inherently opaque nature. Many previous efforts aimed at explaining these predictions rely on input features, specifically, the words within NLP models. However, such explanations are often less informative due to the discrete nature of these words and their lack of contextual verbosity. To address this limitation, we introduce the Latent Concept Attribution method (LACOAT), which generates explanations for predictions based on latent concepts. Our foundational intuition is that a word can exhibit multiple facets, contingent upon the context in which it is used. Therefore, given a word in context, the latent space derived from our training process reflects a specific facet of that word. LACOAT functions by mapping the representations of salient input words into the training latent space, allowing it to provide latent context-based explanations of the prediction.
2023
The Language Model, Resources, and Computational Pipelines for the Under-Resourced Iranian Azerbaijani
Marzia Nouri
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Mahsa Amani
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Reihaneh Zohrabi
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Ehsaneddin Asgari
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
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Co-authors
- Mahsa Amani 1
- Ehsaneddin Asgari 1
- Fahim Dalvi 1
- Nadir Durrani 1
- Hassan Sajjad 1
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