SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations (2023.findings-acl)
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| Challenge: | In many settings, it is important to understand a model’s decision-making process. |
| Approach: | They propose a method for introducing human interpretability in deep language representations by encoding a passage of text as a layer of interpretable categories. |
| Outcome: | The proposed method outperforms existing interpretable language representations on downstream tasks and on agreement with human characterizations of the text. |
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Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer
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