@inproceedings{li-etal-2023-answer,
title = "Answer-state Recurrent Relational Network ({A}s{RRN}) for Constructed Response Assessment and Feedback Grouping",
author = "Li, Zhaohui and
Lloyd, Susan and
Beckman, Matthew and
Passonneau, Rebecca",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.254",
doi = "10.18653/v1/2023.findings-emnlp.254",
pages = "3879--3891",
abstract = "STEM educators must trade off the ease of assessing selected response (SR) questions, like multiple choice, with constructed response (CR) questions, where students articulate their own reasoning. Our work addresses a CR type new to NLP but common in college STEM, consisting of multiple questions per context. To relate the context, the questions, the reference responses, and students{'} answers, we developed an Answer-state Recurrent Relational Network (AsRRN). In recurrent time-steps, relation vectors are learned for specific dependencies in a computational graph, where the nodes encode the distinct types of text input. AsRRN incorporates contrastive loss for better representation learning, which improves performance and supports student feedback. AsRRN was developed on a new dataset of 6,532 student responses to three, two-part CR questions. AsRRN outperforms classifiers based on LLMs, a previous relational network for CR questions, and few-shot learning with GPT-3.5. Ablation studies show the distinct contributions of AsRRN{'}s dependency structure, the number of time steps in the recurrence, and the contrastive loss.",
}
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<abstract>STEM educators must trade off the ease of assessing selected response (SR) questions, like multiple choice, with constructed response (CR) questions, where students articulate their own reasoning. Our work addresses a CR type new to NLP but common in college STEM, consisting of multiple questions per context. To relate the context, the questions, the reference responses, and students’ answers, we developed an Answer-state Recurrent Relational Network (AsRRN). In recurrent time-steps, relation vectors are learned for specific dependencies in a computational graph, where the nodes encode the distinct types of text input. AsRRN incorporates contrastive loss for better representation learning, which improves performance and supports student feedback. AsRRN was developed on a new dataset of 6,532 student responses to three, two-part CR questions. AsRRN outperforms classifiers based on LLMs, a previous relational network for CR questions, and few-shot learning with GPT-3.5. Ablation studies show the distinct contributions of AsRRN’s dependency structure, the number of time steps in the recurrence, and the contrastive loss.</abstract>
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%0 Conference Proceedings
%T Answer-state Recurrent Relational Network (AsRRN) for Constructed Response Assessment and Feedback Grouping
%A Li, Zhaohui
%A Lloyd, Susan
%A Beckman, Matthew
%A Passonneau, Rebecca
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-answer
%X STEM educators must trade off the ease of assessing selected response (SR) questions, like multiple choice, with constructed response (CR) questions, where students articulate their own reasoning. Our work addresses a CR type new to NLP but common in college STEM, consisting of multiple questions per context. To relate the context, the questions, the reference responses, and students’ answers, we developed an Answer-state Recurrent Relational Network (AsRRN). In recurrent time-steps, relation vectors are learned for specific dependencies in a computational graph, where the nodes encode the distinct types of text input. AsRRN incorporates contrastive loss for better representation learning, which improves performance and supports student feedback. AsRRN was developed on a new dataset of 6,532 student responses to three, two-part CR questions. AsRRN outperforms classifiers based on LLMs, a previous relational network for CR questions, and few-shot learning with GPT-3.5. Ablation studies show the distinct contributions of AsRRN’s dependency structure, the number of time steps in the recurrence, and the contrastive loss.
%R 10.18653/v1/2023.findings-emnlp.254
%U https://aclanthology.org/2023.findings-emnlp.254
%U https://doi.org/10.18653/v1/2023.findings-emnlp.254
%P 3879-3891
Markdown (Informal)
[Answer-state Recurrent Relational Network (AsRRN) for Constructed Response Assessment and Feedback Grouping](https://aclanthology.org/2023.findings-emnlp.254) (Li et al., Findings 2023)
ACL