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. |
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| Challenge: | Existing methods for fact checking use string concatenation or fusing features of isolated evidence sentences. |
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| Challenge: | Existing fact-checking datasets do not provide manual annotations for sentence-level evidence. |
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| Challenge: | Existing methods for fact extraction and verification combine all evidence sentences to produce redundant information. |
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| Challenge: | Existing methods to fact-check information focus on word-level attention or evidence-level focus, which may result in suboptimal performance. |
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| Challenge: | Existing methods for fact verification use pretrained sequence-to-sequence transformers for sentence selection and label prediction. |
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DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking (2020.acl-main)
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Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab, Smaranda Muresan
| Challenge: | Fact Extraction and Verification datasets provide a resource for end-to-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. |
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Fine-grained Fact Verification with Kernel Graph Attention Network (2020.acl-main)
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| Challenge: | Existing methods for fact verification are based on dot-product attentions, but kernel-based attentions focus more on relevant evidence sentences and meaningful clues in the evidence graph. |
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Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification (2021.eacl-main)
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| Challenge: | Existing research on fact verification focuses on unstructured textual evidence, but it is still underexplored. |
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