Papers by Marc Brinner
Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training (2024.emnlp-main)
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| Challenge: | Neural networks are increasingly prevalent across a wide range of applications, driving significant advancements in fields such as natural language processing, computer vision, and beyond. |
| Approach: | They propose an end-to-end differentiable training paradigm for stable training of a rationalized transformer classifier. |
| Outcome: | The proposed model is capable of classifying a sample and scoring input tokens without any explicit supervision and produces class-wise rationales without instabilities. |
SemCSE-Multi: Multifaceted and Decodable Embeddings for Aspect-Specific and Interpretable Scientific Domain Mapping (2026.acl-long)
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| Challenge: | Existing embedding models rely on implicit, imprecise and fixed notion of similarity to evaluate scientific abstracts. |
| Approach: | They propose a framework for generating multifaceted embeddings of scientific abstracts . they propose an unsupervised procedure that produces aspect-specific summarizing sentences . |
| Outcome: | The proposed framework captures distinct, individually specifiable aspects in isolation . it then trains embedding models to map semantically related summaries to nearby positions . the proposed framework is evaluated in the domains of invasion biology and medicine . |
Model Interpretability and Rationale Extraction by Input Mask Optimization (2023.findings-acl)
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| Challenge: | Existing methods for creating explanations for black-box models struggle with deriving easily interpretable explanations. |
| Approach: | They propose a model-agnostic method to generate extractive explanations for neural network predictions using masking parts of the input that the model does not consider indicative of the respective class. |
| Outcome: | The proposed method achieves state-of-the-art results in a paragraph-level rationale extraction task, showing that this task can be performed without training a specialized model. |