Papers by Chris Reed
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. |
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation (2020.lrec-1)
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| Challenge: | a corpus of 2016 debates and commentary contains 4,648 argumentative propositions annotated with fine-grained proposition types. |
| Approach: | They propose a machine learning-human workflow for annotating for four complex proposition types . they demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates . |
| Outcome: | The proposed method can be used by technical researchers seeking more nuanced representations of argument . it can also be used to analyze rhetorical strategies and structure in presidential debates . |
Advances in Argument Mining (P19-4)
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| Challenge: | Argument mining is a rapidly growing area of research and research that has seen significant growth over the past few years. |
| Approach: | Argument mining is a new area of research that uses opinion mining to extract opinions . the 6th ACL workshop on argument mining will be in Florence in 2019 . |
| Outcome: | Argument mining is a new area of research and development that has seen significant growth in the past three years. |
Knowledge-Enhanced Evidence Retrieval for Counterargument Generation (2021.findings-emnlp)
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| Challenge: | Existing models lack the reasoning abilities needed to find complex counterevidence. |
| Approach: | They propose a natural language inference model that finds counterevidence from diverse sources on the Web. |
| Outcome: | The proposed model outperforms baseline models for NLI tasks and finds complex counterevidence better. |
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. |
The RIP Corpus of Collaborative Hypothesis-Making (2024.lrec-main)
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| Challenge: | Existing studies on hypothesis generation and collaborative problem solving combine these two fields but there is still a gap between the two. |
| Approach: | They propose to use a fictionalised murder investigation game as an environment to investigate how hypotheses are generated in group environments. |
| Outcome: | The proposed corpus shows the emergent roles individuals took on and the strategies the groups employed, showing what can be gained through a deeper exploration of this domain. |
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. |
Detecting Attackable Sentences in Arguments (2020.emnlp-main)
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| Challenge: | Prior work in NLP studies focus on argument quality and making counterarguments toward the main claim, without investigating what parts of an argument are attackable for successful persuasion. |
| Approach: | They propose to use machine learning to find attackable sentences in online arguments by analyzing driving reasons for attacks and identifying relevant characteristics of sentences. |
| Outcome: | The proposed model can detect attackable sentences significantly better than baselines and comparably well to laypeople. |
Extracting Implicitly Asserted Propositions in Argumentation (2020.emnlp-main)
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| Challenge: | Argumentation is a rhetorical device that asserts propositions implicitly, but few studies have examined the issue. |
| Approach: | They propose a computational method for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation. |
| Outcome: | The proposed models are based on a corpus of 2016 debates and online commentary. |
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. |
Automating Alternative Generation in Decision-Making (2025.findings-emnlp)
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| Challenge: | Cognitive biases can impede decision making by constraining individual decision makers’ creativity. |
| Approach: | They propose a task for automatically generating alternative options based on atomic action components and a dataset of 106 annotated Reddit r/Advice posts containing unique alternative options extracted from users’ replies. |
| Outcome: | The proposed task is based on 106 annotated Reddit r/Advice posts containing unique alternative options extracted from users’ replies. |
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes (2021.tacl-1)
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| Challenge: | Recent studies have focused on training complex neural networks on labeled data. |
| Approach: | They propose to use logical mechanisms to classify argumentative relations without training on labeled data. |
| Outcome: | The proposed method classifies argumentative relations without training on labeled data significantly better than unsupervised baselines. |
Lexical Recall or Logical Reasoning: Probing the Limits of Reasoning Abilities in Large Language Models (2025.acl-long)
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| Challenge: | Existing work on LLMs assesses logic abilities independently from lexical memory. |
| Approach: | They propose to assess LLMs' logic abilities independently from lexical memory . they use two sets of grid puzzles in 42 different sizes and 12 difficulty levels . |
| Outcome: | The proposed benchmarks show that LLMs are limited to a few steps of reasoning . the results show that the applied obfuscation strategies help mitigate effects of logic puzzles being part of training data. |
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. |
QT30: A Corpus of Argument and Conflict in Broadcast Debate (2022.lrec-1)
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| Challenge: | Broadcast political debate is the public's easiest access to opinions that shape policies and enables the general public to make informed choices. |
| Approach: | They present the largest corpus of analysed dialogical argumentation ever created using 30 episodes of BBC's 'Question Time' from 2020 and 2021. |
| Outcome: | The resource is freely available at http://corpora.aifdb.org/qt30. |
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. |
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. |