Papers by Shoichi Naito
LLM DEBATE OPPONENT : Counter-argument Generation focusing on Implicit and Critical Premises (2025.naacl-srw)
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| Challenge: | Recent advances in Large Language Models (LLMs) show promise in automating counter-argument generation. |
| Approach: | They compare multi-step and one-step generation methods for counter-arguments across 100 debate topics. |
| Outcome: | The proposed model outperforms multi-step and one-step pipelines for counter-arguments across 100 debate topics. |
Identification of Multiple Logical Interpretations in Counter-Arguments (2025.emnlp-main)
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Wenzhi Wang, Paul Reisert, Shoichi Naito, Naoya Inoue, Machi Shimmei, Surawat Pothong, Jungmin Choi, Kentaro Inui
| Challenge: | Counter-arguments (CAs) are a good way to improve learners' critical thinking skills . however, it is difficult to provide every learner tailored feedback due to limited human resources and heavy workloads. |
| Approach: | They propose to annotate a dataset of 134 CAs annotated with 13 logical predicate questions and train a model with Reinforcement Learning with Verifiable Rewards to identify multiple logical interpretations. |
| Outcome: | The proposed model performs on par with larger proprietary models. |
Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling (2024.emnlp-main)
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Irfan Robbani, Paul Reisert, Surawat Pothong, Naoya Inoue, Camélia Guerraoui, Wenzhi Wang, Shoichi Naito, Jungmin Choi, Kentaro Inui
| Challenge: | Prior work on quality assessment has focused on numerical scoring and fallacy type-labeling tasks, without aiming to analyze fallacy logic structures. |
| Approach: | They propose four sets of explainable templates for common informal logical fallacies designed to explicate a fallacy’s implicit logic. |
| Outcome: | The proposed models achieve a high agreement score and reasonable coverage 83% on 400 fallacious arguments and state-of-the-art language models struggle with detecting fallacy templates (0.47 accuracy). |
IRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments (2022.lrec-1)
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| Challenge: | Using crowdsourcing, we show that models trained with domain-specific implicit reasonings outperform domain-general models in both automatic and human evaluations. |
| Approach: | They propose to create a domain-specific corpus of implicit reasonings annotated for a wide range of arguments and use it to generate models. |
| Outcome: | The proposed corpus outperforms domain-general models in automatic and human evaluations. |
LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments (2022.lrec-1)
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| Challenge: | Argumentation plays a central role in human communication, where refuting or attacking others’ arguments is a common persuasion strategy. |
| Approach: | They propose a novel annotation scheme that captures common modes and complex rhetorical moves in attacks along with the implicit presuppositions and value judgments. |
| Outcome: | The proposed scheme shows moderate agreement between the two annotations, indicating that human annotation is feasible. |
TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation (2022.lrec-1)
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Shoichi Naito, Shintaro Sawada, Chihiro Nakagawa, Naoya Inoue, Kenshi Yamaguchi, Iori Shimizu, Farjana Sultana Mim, Keshav Singh, Kentaro Inui
| Challenge: | Argumentation and debate are effective tools for developing critical thinking skills, but it requires a lot of time and effort. |
| Approach: | They propose to automate the process of giving diagnostic comments to students . they define criteria for a template set that can be used to evaluate the model . |
| Outcome: | The proposed model can be used to evaluate arguments and evaluate them in real time. |
Designing Logic Pattern Templates for Counter-Argument Logical Structure Analysis (2024.findings-emnlp)
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Shoichi Naito, Wenzhi Wang, Paul Reisert, Naoya Inoue, Camélia Guerraoui, Kenshi Yamaguchi, Jungmin Choi, Irfan Robbani, Surawat Pothong, Kentaro Inui
| Challenge: | Despite their effectiveness, the logical attack structure of counterarguments remains unexplored due to its complexity. |
| Approach: | They propose a task to analyze logical attack structure of counterarguments in relation to their corresponding opponent argument using 10 new CA logic patterns. |
| Outcome: | The proposed task achieves high annotator agreement and coverage and high coverage on a dataset of 778 CAs. |