| 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. |
| Outcome: | The proposed framework outperforms baselines on the NewsClaims benchmark. |
Similar Papers
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge (2022.emnlp-main)
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Revanth Gangi Reddy, Sai Chetan Chinthakindi, Zhenhailong Wang, Yi Fung, Kathryn Conger, Ahmed ELsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji
| Challenge: | Current claims detection methods focus on sentence analysis, ignoring other attributes . a key element of identifying misinformation is detecting the claims and the arguments that have been presented. |
| Approach: | They propose a benchmark for attribute-aware claim detection in the news domain . they extend the problem to include extraction of additional attributes related to each claim . |
| Outcome: | The proposed system performs well on the test, but human performance is still poor. |
Leveraging fine-tuned Large Language Models with LoRA for Effective Claim, Claimer, and Claim Object Detection (2024.eacl-long)
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| Challenge: | Existing work on identifying claims has focused on sentence level, neglecting supplementary attributes such as the claimer and claim object of the claim. |
| Approach: | They propose a novel approach to detect claims using large language models in natural language understanding and text generation. |
| Outcome: | The proposed approach transforms claim, claimer and claim object detection task into QA setting. |
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights (2021.acl-short)
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| Challenge: | Existing methods for event extraction require expensive annotation and are not extensible to new event ontologies. |
| Approach: | They propose to use textual entailment and/or question answering queries to extract a zero-shot event from a set of TE and/ or QA queries. |
| Outcome: | The proposed method achieves acceptable results on ACE-2005 and ERE, but there is still a large gap from supervised approaches. |
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking (2025.coling-main)
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| Challenge: | Claim matching (CM) is a binary classification task that can be used to determine if two claims can be verified using the same piece of evidence or fact-check. |
| Approach: | They propose a claim matching task that uses binary classification and large language models to test out learning approaches to the task. |
| Outcome: | The proposed task can be tackled by leveraging mature tasks such as natural language inference or paraphrase detection. |
Adversarial Learning for Zero-Shot Stance Detection on Social Media (2021.naacl-main)
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| Challenge: | a new model for zero-shot stance detection on Twitter uses adversarial learning to generalize across topics . previous work on zero- shot stance detector on English social media focuses on cross-target stances . |
| Approach: | They propose a model that uses adversarial learning to generalize across topics on Twitter . their model achieves state-of-the-art performance on unseen test topics . |
| Outcome: | The proposed model achieves state-of-the-art performance on unseen topics with minimal computational costs. |
Towards an argumentative content search engine using weak supervision (C18-1)
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| 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. |
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning (2024.lrec-main)
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| Challenge: | Stance Reasoner is a model for zero-shot stance detection on social media platforms that can be used to extract opinions from opinionated content. |
| Approach: | They propose a method that leverages explicit reasoning over background knowledge to guide the model’s inference about the document’s stance on a target. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on 3 Twitter datasets, including fully supervised models. |
Towards Effective Extraction and Evaluation of Factual Claims (2025.acl-long)
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| Challenge: | Lack of a standardized evaluation framework impedes assessment and comparison of claim extraction methods. |
| Approach: | They propose a framework for evaluating claim extraction in the context of fact-checking . they also introduce Claimify, an LLM-based claim extraction method . |
| Outcome: | The proposed evaluation framework outperforms existing methods in the evaluation of claim extraction methods. |
Weakly-Supervised Questions for Zero-Shot Relation Extraction (2023.eacl-main)
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| Challenge: | Zero-Shot Relation Extraction (ZRE) is a task where the training and test sets have no shared relation types. |
| Approach: | They propose to learn a model that can translate relation descriptions into relevant questions, which are then leveraged to generate the correct tail entity. |
| Outcome: | The proposed model outperforms the state-of-the-art on the fewrel and WikiZSL datasets by more than 16 F1 points without using gold question templates. |
Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations (2020.emnlp-main)
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| Challenge: | Existing methods for stance detection are topic-specific and cross-target stance. |
| Approach: | They propose a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. |
| Outcome: | The proposed model improves performance on a number of challenging linguistic phenomena. |