Papers by Debela Gemechu
CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining (2025.acl-long)
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| Challenge: | Argument Mining (AM) involves the automatic identification of argument structure in natural language. |
| Approach: | They propose an approach that captures local and global coherence to identify argument structures by modeling macro-structure. |
| Outcome: | The proposed approach shows superior performance on heterogeneous datasets and on unseen datasets. |
Decompositional Argument Mining: A General Purpose Approach for Argument Graph Construction (P19-1)
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| Challenge: | Argument mining is the process of identifying argumentative structure contained within a text. |
| Approach: | They propose to decompose propositions into four functional components and identify the patterns linking those components to determine argument structure. |
| Outcome: | The proposed method is generic in that it is not tuned for a specific corpus and achieved an F score of 0.79, 0.77 and 0.64 respectively. |
Natural Language Reasoning in Large Language Models: Analysis and Evaluation (2025.findings-acl)
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| Challenge: | Argumentative reasoning presents unique challenges due to its reliance on context, implicit assumptions, and value judgments. |
| Approach: | They propose a large-scale evaluation of LLMs' unconstrained natural language reasoning capabilities . they formalise a new strategy designed to evaluate argumentative reasoning in LLM . |
| Outcome: | The proposed model performs better on a range of reasoning tasks than other models. |
External Knowledge-Driven Argument Mining: Leveraging Attention-Enhanced Multi-Network Models (2024.emnlp-main)
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| Challenge: | Argument mining involves the identification of argument relations (AR) between Argumentative Discourse Units (ADUs). |
| Approach: | They propose to leverage external resources to identify semantic paths linking ADUs . they propose to use WordNet, ConceptNet, and Wikipedia to identify these paths . |
| Outcome: | The proposed architecture achieves F-scores of 0.85, 0.84, 0.70, and 0.87 on four datasets. |
Looking at the Unseen: Effective Sampling of Non-Related Propositions for Argument Mining (2025.coling-main)
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| Challenge: | Argument mining is the task of automatically identifying argumentative structures in natural language documents. |
| Approach: | They propose to use context and semantic similarity to sample non-related propositions . argument mining is the task of automatically identifying argumentative structures in natural language documents . |
| Outcome: | The proposed sampling strategies improve the performance of argument mining tasks. |
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. |