Papers with label accuracy
Unsupervised Question Answering for Fact-Checking (D19-66)
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| Challenge: | Recent Deep Learning (DL) models have achieved human-level accuracy on natural language tasks such as question-answering, natural language inference, and textual entailment. |
| Approach: | They propose an unsupervised question-answering based approach for a similar task, fact-checking. |
| Outcome: | The proposed approach achieves label accuracy of 80.2% on the development set and 80.25% on the test set. |
Exploring Listwise Evidence Reasoning with T5 for Fact Verification (2021.acl-short)
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| Challenge: | Existing methods for fact verification use pretrained sequence-to-sequence transformers for sentence selection and label prediction. |
| Approach: | They propose a framework for fact verification that leverages pretrained sequence-to-sequence transformer models for sentence selection and label prediction. |
| Outcome: | The proposed framework scores higher than the second place approach on the blind test set . the proposed framework can be useful for a broader range of NLP tasks, the authors say . |
ProoFVer: Natural Logic Theorem Proving for Fact Verification (2022.tacl-1)
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| Challenge: | Recent fact verification systems rely on neural network classifiers for veracity prediction, which lack explainability. |
| Approach: | They propose a model that generates natural logic-based inferences as proofs using lexical mutations between spans in the claim and the evidence retrieved. |
| Outcome: | The proposed model has highest label accuracy and second best score in the FEVER leaderboard. |
Praat++: Multimedia Annotation System for Speech and Vocalization (2026.acl-demo)
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| Challenge: | High-quality time-aligned annotation is fundamental to speech processing and animal vocalization research, yet precise boundary localization and consistent labeling remain challenging in collaborative settings. |
| Approach: | They propose a web-based multimedia annotation system for collaborative, video-informed, and AI-assisted timeline labeling of audio and video data. |
| Outcome: | The proposed system improves time-aligned labeling and accuracy in speech and animal vocalization annotations. |
IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification (2022.coling-1)
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| Challenge: | Existing models for fact extraction and verification fail to utilize multi-view contextual information. |
| Approach: | They propose to integrate multi-view contextual information (IMCI) for fact extraction and verification by combining contextual information with inter-document context. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the open-domain Wikipedia task with a winning FEVER score of 73.96% and label accuracy of 77.25% on the online blind test set. |
A Multi-Level Attention Model for Evidence-Based Fact Checking (2021.findings-acl)
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| Challenge: | Recent state-of-the-art approaches have developed increasingly sophisticated models based on graph structures. |
| Approach: | They propose a simple model that can be trained on sequence structures and can benefit from joint training. |
| Outcome: | The proposed model outperforms the graph-based models on a large-scale dataset for Fact Extraction and VERification. |