Papers by John Lawrence
Mining Complex Patterns of Argumentative Reasoning in Natural Language Dialogue (2025.acl-long)
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| Challenge: | Argumentation scheme mining is the task of automatically identifying reasoning mechanisms behind argument inferences. |
| Approach: | They propose to create a corpus of 441 arguments annotated with 24 argumentation schemes and leverage the capabilities of LLMs and Transformer-based models to validate their applicability in real-world scenarios. |
| Outcome: | The proposed corpus of arguments is pre-trained on a large corpus containing textbook-like argumentation schemes and validates their applicability in real-world scenarios. |
FORECAST2023: A Forecast and Reasoning Corpus of Argumentation Structures (2024.lrec-main)
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| Challenge: | Existing work on the role of reasoning in forecasting has focused on surface-level features such as linguistic markers, the use of comparison classes, and overall dialectical complexity. |
| Approach: | They propose to use a dataset of such prediction rationales to create a fully automated annotation system that can be used to enhance the argumentation. |
| Outcome: | The proposed dataset provides a uniquely fine-grained and close characterisation of the structure of argumentation with potential impact on forecasting domains from intelligence analysis to investment decision-making. |
Intertextual Correspondence for Integrating Corpora (L18-1)
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| Challenge: | Using intertextual correspondence, we can combine annotated text corpora to create new annotation connections. |
| Approach: | They propose to use intertextual correspondence as an integrative technique for combining annotated text corpora. |
| Outcome: | The proposed technique can be used to build argumentative arguments in two annotated text corpora. |
The Open Argument Mining Framework (2025.acl-demo)
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Debela Gemechu, Ramon Ruiz-Dolz, Kamila Górska, Somaye Moslemnejad, Eimear Maguire, Dimitra Zografistou, Yohan Jo, John Lawrence, Chris Reed
| Challenge: | Argument Mining (AM) has been a key area of research for many years, but it is still a challenging field. |
| Approach: | the oAMF provides an open-source, modular platform that unifies diverse AM methods. |
| Outcome: | the oAMF is an open-source, modular, and scalable platform that unifies diverse AM methods. |