| Challenge: | Existing approaches focus on generating single claims, but there are limitations. |
| Approach: | They propose to use a triplet neural network to infer a conclusion's target from premises' targets and a neural network for a new target. |
| Outcome: | The proposed approach outperforms baselines on two domains. |
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Conclusion-based Counter-Argument Generation (2023.eacl-main)
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| Challenge: | Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. |
| Approach: | They propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. |
| Outcome: | The proposed approach generates more relevant and stance-adhering counters than strong baselines. |
Generating Informative Conclusions for Argumentative Texts (2021.findings-acl)
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| Challenge: | Argumentative texts often omit explicit conclusions, expecting readers to infer them rather . a corpus of 136,996 arguments is compiled and used to generate informative conclusions . |
| Approach: | They propose to generate informative conclusions from a large-scale corpus of argumentative texts . they propose to use argumentative knowledge to augment the corpus and refine the model . |
| Outcome: | The proposed corpus of argumentative texts and their conclusions is compiled and analyzed . the results show that the proposed model is informative and concise . |
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. |
Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks (2023.emnlp-main)
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| Challenge: | Existing methods for analyzing argumentative debates are insufficient to understand complex tasks. |
| Approach: | They propose a hybrid method to automatically predict the winning stance in argumentative debates using arguments from argumentation theory and semantics. |
| Outcome: | The proposed method is based on an unexplored new instance of the automatic analysis of natural language arguments. |
Neural Argument Generation Augmented with Externally Retrieved Evidence (P18-1)
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| Challenge: | Existing methods for generating arguments are limited to retrieval-based methods. |
| Approach: | They propose an encoder-decoder-based argument generation model enriched with externally retrieved evidence from Wikipedia. |
| Outcome: | The proposed model generates arguments with more topic-relevant content than current models based on automatic evaluation and human assessments on a large-scale dataset from reddit. |
Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing (2023.findings-eacl)
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| Challenge: | Using the essay context of the revision and feedback from students prior to the revision, we identify desirable and undesirable revisions. |
| Approach: | They propose to use the essay context of the revision and the feedback students received before the revision to improve classifier performance. |
| Outcome: | The proposed models improve over baseline models, while models utilizing context improve over the baseline models. |
Unsupervised Argumentation Mining in Student Essays (2020.lrec-1)
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| Challenge: | State-of-the-art argumentation mining systems rely on annotated training data and are supervised, thus relying on an annotation of the components and relationships between them. |
| Approach: | They propose to bootstrap from a small set of argument components automatically identified using simple heuristics in combination with reliable contextual cues. |
| Outcome: | The proposed approach outperforms two supervised baselines and achieves 73.5-83.7% of the performance of a state-of-the-art neural approach. |
Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates (2022.emnlp-main)
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| Challenge: | Existing work on persuasion in online forums focuses on identifying debate winners and winning negotiation games. |
| Approach: | They adopt a hierarchical generative Variational Autoencoder model to model winning arguments . they propose competing hypotheses about the nature of argumentation . |
| Outcome: | The proposed model predicts winning arguments in reddit debates . it uses a hierarchical generative Variational Autoencoder to model argumentation . |
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. |
Leveraging Topic Relatedness for Argument Persuasion (2021.findings-acl)
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| Challenge: | Existing studies of argumentation focus on the effects of factors such as source, audience, and language style, but the impact of exploiting the relationships among controversial topics is under-explored. |
| Approach: | They propose to model topic relatedness among controversial topics using topic embedding features and topic semantics features extracted from the arguments. |
| Outcome: | The proposed method improves predicting persuasiveness and generalizes to rare topics in a few-shot setting. |