Papers by Rebecca J. Passonneau
Improving Model Evaluation using SMART Filtering of Benchmark Datasets (2025.naacl-long)
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| Challenge: | Creating high quality human-annotated datasets is difficult due to dataset saturation. |
| Approach: | They propose a method to filter a subset of test examples from existing benchmarks by removing less informative and lower quality examples. |
| Outcome: | The proposed method reduces dataset size by 48% while increasing Pearson correlation with rankings from ChatBot Arena. |
A Semantic Feature-Wise Transformation Relation Network for Automatic Short Answer Grading (2021.emnlp-main)
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| Challenge: | Multiple choice questions can be graded automatically, but automated short answer grading is time-consuming and has bias and errors. |
| Approach: | They propose a Semantic Feature-wise transformation Relation Network that captures relational knowledge among the questions, reference answers or rubrics and labeled student answers. |
| Outcome: | The proposed model has up to 11% performance improvement over state-of-the-art approaches on the benchmark SemEval-2013 datasets, and surpasses custom approaches designed for a Kaggle challenge. |
Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
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| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
| Approach: | This tutorial introduces contrastive learning and provides an introduction to the techniques. |
| Outcome: | This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them. |
♪ Something Just Like TRuST ♪ *: Toxicity Recognition of Span and Target (2026.findings-acl)
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| Challenge: | Toxic language is pervasive online, and because LLMs are trained on web data, it generates such content. |
| Approach: | They propose a large-scale dataset that synthesizes toxicity definitions and an annotation scheme . they use a rigorous human annotation process to evaluate the diversity of the annotations . |
| Outcome: | The proposed model outperforms existing models on three tasks and is not reliable. |
ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences (2021.acl-long)
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| Challenge: | Existing work relies on rule-based methods dependent on parsing to identify atomic sentences. |
| Approach: | They propose a task to decompose complex sentences into simple ones . they propose atomic clauses as atomic sentences, and a graph edit task to predict edits . |
| Outcome: | The proposed model performs better than baselines on MinWiki and DeSSE. |