| Challenge: | Existing work focused on detecting claims within a small set of documents . however, pinpointing relevant claims within massive unstructured corpora, received little attention. |
| Approach: | They propose to use a weak signal to develop a query for claim–sentence detection using a large text corpus. |
| Outcome: | The proposed system outperforms previous results in terms of precision and coverage. |
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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. |
ArgumenText: Searching for Arguments in Heterogeneous Sources (N18-5)
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Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych
| Challenge: | Argument mining is a core technology for enabling argument search in large corpora . but current methods fail when applied to heterogeneous texts . despite its obvious applications, argument search has attracted relatively little attention . |
| Approach: | They propose a system that searches sentential arguments for any given topic . ArgumenText automatically identifies and classifies arguments by relevance . |
| Outcome: | The proposed system covers 89% of arguments found in expert-curated lists . it also identifies additional valid arguments omitted or overlooked by human curators . |
Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain (2024.lrec-main)
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| Challenge: | Argument mining is a complex process that requires a large amount of resources and time. |
| Approach: | They propose to analyze arguments in three different languages and domains to understand their robustness to natural language variations. |
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Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset (N18-2)
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| Challenge: | Argument mining is a subset of NLP that deals with extracting arguments from user-based content. |
| Approach: | They propose to use weakly supervised and semi-supervised methods to automatically annotate reviews and provide large annotated datasets. |
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Ad-hoc Document Retrieval using Weak-Supervision with BERT and GPT2 (2020.emnlp-main)
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| Challenge: | a weakly-supervised method is used for document retrieval tasks . traditional methods are used for ad-hoc querying, but they require large amounts of labeled data . |
| Approach: | They propose a weakly-supervised method for training deep learning models for ad-hoc document retrieval using weak-supervision from the documents in the corpus. |
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Can We Identify Stance without Target Arguments? A Study for Rumour Stance Classification (2024.lrec-main)
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| Challenge: | Existing target-aware models underperform in cases where the context of the target is crucial. |
| Approach: | They propose a framework to enhance reasoning with the targets and propose 'target-aware' models without awareness of the target. |
| Outcome: | The proposed framework achieves state-of-the-art on two benchmark datasets. |
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. |
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TARGER: Neural Argument Mining at Your Fingertips (P19-3)
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Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, Alexander Panchenko
| Challenge: | Argumentation is a multi-disciplinary field that extends from philosophy and psychology to linguistics as well as to artificial intelligence. |
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Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
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Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
| Challenge: | Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures. |
| Approach: | They propose to use Wikipedia sections to extract weak labels for sentences describing economic events from text. |
| Outcome: | The proposed method can extract weak labels for sentences describing economic events from Wikipedia sentences. |
A Zero-Shot Claim Detection Framework Using Question Answering (2022.coling-1)
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| Challenge: | Existing claims detection frameworks are portability to emerging events and low-resource training data settings. |
| Approach: | They propose a claim detection framework that leverages zero-shot Question Answering to solve sub-tasks such as topic filtering, claim object detection, and claimer detection. |
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