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    <abstract>Automatic Short Answer Scoring (ASAS) requires models that can score student responses against question-specific criteria while remaining efficient and transferable across rubric sets. We propose RUSPAN, a rubric-conditioned ASAS framework that treats rubric descriptions as semantic label representations. RUSPAN serialises the question context, student answer, and all candidate rubric levels into a single sequence, then scores the levels listwise from the rubric-span and whole-sequence representations produced in a single LM pass. We further introduce RUSPAN-RIM, in which a Rubric-Independent Mask prevents rubric spans from attending to one another, making rubric representations depend only on the answer and question context and preventing overfitting to rubric patterns during training for zero-shot transfer. On six ASAS benchmarks spanning English, German, and Portuguese, RUSPAN improves mono-benchmark scoring over discriminative and generative baselines, while RIM with position reindexing delivers consistent and substantial gains on PT-ASAG, the held-out benchmark with the strongest combined language and rubric-structure shift.</abstract>
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%0 Conference Proceedings
%T Rubric Spans are Label Representations: Joint LLM Encoding for Short Answer Scoring
%A Sun, Zhifan
%A Gombert, Sebastian
%A Zehner, Fabian
%A Camus, Leon
%A Cong, Longwei
%A Drachsler, Hendrik
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F sun-etal-2026-rubric
%X Automatic Short Answer Scoring (ASAS) requires models that can score student responses against question-specific criteria while remaining efficient and transferable across rubric sets. We propose RUSPAN, a rubric-conditioned ASAS framework that treats rubric descriptions as semantic label representations. RUSPAN serialises the question context, student answer, and all candidate rubric levels into a single sequence, then scores the levels listwise from the rubric-span and whole-sequence representations produced in a single LM pass. We further introduce RUSPAN-RIM, in which a Rubric-Independent Mask prevents rubric spans from attending to one another, making rubric representations depend only on the answer and question context and preventing overfitting to rubric patterns during training for zero-shot transfer. On six ASAS benchmarks spanning English, German, and Portuguese, RUSPAN improves mono-benchmark scoring over discriminative and generative baselines, while RIM with position reindexing delivers consistent and substantial gains on PT-ASAG, the held-out benchmark with the strongest combined language and rubric-structure shift.
%U https://aclanthology.org/2026.emnlp-main.662/
%P 15246-15266

Markdown (Informal)

[Rubric Spans are Label Representations: Joint LLM Encoding for Short Answer Scoring](https://aclanthology.org/2026.emnlp-main.662/) (Sun et al., EMNLP 2026)

• Rubric Spans are Label Representations: Joint LLM Encoding for Short Answer Scoring (Sun et al., EMNLP 2026)

ACL

• Zhifan Sun, Sebastian Gombert, Fabian Zehner, Leon Camus, Longwei Cong, and Hendrik Drachsler. 2026. Rubric Spans are Label Representations: Joint LLM Encoding for Short Answer Scoring. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15246–15266, Budapest, Hungary. Association for Computational Linguistics.