@inproceedings{pothong-etal-2025-focus,
title = "{FOCUS}: A Benchmark for Targeted Socratic Question Generation via Source-Span Grounding",
author = "Pothong, Surawat and
Shimmei, Machi and
Inoue, Naoya and
Reisert, Paul and
Brassard, Ana and
Wang, Wenzhi and
Naito, Shoichi and
Choi, Jungmin and
Inui, Kentaro",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.157/",
pages = "2938--2958",
ISBN = "979-8-89176-298-5",
abstract = "We present FOCUS, a benchmark and task setting for Socratic question generation that delivers more informative and targeted feedback to learners. Unlike prior datasets, which rely on broad typologies and lack grounding in the source text, FOCUS introduces a new formulation: each Socratic question is paired with a fine-grained, 11-type typology and an explicit source span from the argument it targets. This design supports clearer, more actionable feedback and facilitates interpretable model evaluation. FOCUS includes 440 annotated instances with moderate partial-match agreement, establishing it as a reliable benchmark. Baseline experiments with representative state-of-the-art models reveal, through detailed error analysis, that even strong models struggle with span selection and context-sensitive categories. An extension study on the LogicClimate dataset further confirms the generalizability of the task and annotation framework. FOCUS sets a new standard for pedagogically grounded and informative Socratic question generation."
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<abstract>We present FOCUS, a benchmark and task setting for Socratic question generation that delivers more informative and targeted feedback to learners. Unlike prior datasets, which rely on broad typologies and lack grounding in the source text, FOCUS introduces a new formulation: each Socratic question is paired with a fine-grained, 11-type typology and an explicit source span from the argument it targets. This design supports clearer, more actionable feedback and facilitates interpretable model evaluation. FOCUS includes 440 annotated instances with moderate partial-match agreement, establishing it as a reliable benchmark. Baseline experiments with representative state-of-the-art models reveal, through detailed error analysis, that even strong models struggle with span selection and context-sensitive categories. An extension study on the LogicClimate dataset further confirms the generalizability of the task and annotation framework. FOCUS sets a new standard for pedagogically grounded and informative Socratic question generation.</abstract>
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%0 Conference Proceedings
%T FOCUS: A Benchmark for Targeted Socratic Question Generation via Source-Span Grounding
%A Pothong, Surawat
%A Shimmei, Machi
%A Inoue, Naoya
%A Reisert, Paul
%A Brassard, Ana
%A Wang, Wenzhi
%A Naito, Shoichi
%A Choi, Jungmin
%A Inui, Kentaro
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F pothong-etal-2025-focus
%X We present FOCUS, a benchmark and task setting for Socratic question generation that delivers more informative and targeted feedback to learners. Unlike prior datasets, which rely on broad typologies and lack grounding in the source text, FOCUS introduces a new formulation: each Socratic question is paired with a fine-grained, 11-type typology and an explicit source span from the argument it targets. This design supports clearer, more actionable feedback and facilitates interpretable model evaluation. FOCUS includes 440 annotated instances with moderate partial-match agreement, establishing it as a reliable benchmark. Baseline experiments with representative state-of-the-art models reveal, through detailed error analysis, that even strong models struggle with span selection and context-sensitive categories. An extension study on the LogicClimate dataset further confirms the generalizability of the task and annotation framework. FOCUS sets a new standard for pedagogically grounded and informative Socratic question generation.
%U https://aclanthology.org/2025.ijcnlp-long.157/
%P 2938-2958
Markdown (Informal)
[FOCUS: A Benchmark for Targeted Socratic Question Generation via Source-Span Grounding](https://aclanthology.org/2025.ijcnlp-long.157/) (Pothong et al., IJCNLP-AACL 2025)
ACL
- Surawat Pothong, Machi Shimmei, Naoya Inoue, Paul Reisert, Ana Brassard, Wenzhi Wang, Shoichi Naito, Jungmin Choi, and Kentaro Inui. 2025. FOCUS: A Benchmark for Targeted Socratic Question Generation via Source-Span Grounding. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 2938–2958, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.