Papers with interpretable representations
Dependency parsing with structure preserving embeddings (2021.eacl-main)
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| Challenge: | Modern neural approaches to dependency parsing are trained to predict a tree structure by learning a contextual representation for tokens in a sentence and a head–dependent scoring function. |
| Approach: | They propose to combine a contextual representation for tokens and a head–dependent scoring function to learn interpretable representations by training a parser to explicitly preserve structural properties of a tree. |
| Outcome: | The proposed approach yields strong tree distance preservation and parsing performance on par with a competitive graph-based parser. |
Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health (2021.findings-emnlp)
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| Challenge: | Existing statistical models are not explainable, struggle in low-resource scenarios and cannot be reused for multiple tasks. |
| Approach: | They propose a micromodel architecture that embeds domain knowledge and provides explanations throughout the model’s decision process. |
| Outcome: | The proposed model is validated on depression classification, PTSD classification, and suicidal risk assessment tasks. |
Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision (2025.naacl-long)
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Zhouhang Xie, Tushar Khot, Bhavana Dalvi Mishra, Harshit Surana, Julian McAuley, Peter Clark, Bodhisattwa Prasad Majumder
| Challenge: | Instruction-following LLMs have recently allowed systems to discover hidden concepts from a collection of unstructured documents based on a natural language description of the purpose of the discovery (i.e., goal). |
| Approach: | They propose a goal-oriented latent factor discovery system that integrates LLM’s instruction-following ability with statistical models to handle large, noisy datasets where LLM reasoning alone falls short. |
| Outcome: | The proposed system improves task performance by 5-52% over baselines and 1.8 times as often as the best alternative, on average, in human evaluation. |
PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder (2025.acl-long)
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| Challenge: | Existing embedding models excel at capturing general meaning, but overlook ideological nuances, limiting their effectiveness in political bias tasks. |
| Approach: | They propose a framework to Produce inteRpretable polItical biaS eMbeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding models in political bias classification . the proposed framework offers highly interpretable representations for political analysis . |