Papers with Fact Extraction
FEVER: a Large-scale Dataset for Fact Extraction and VERification (N18-1)
Copied to clipboard
| Challenge: | 185,445 claims generated by altering sentences from Wikipedia are verified without knowledge of the sentence they were derived from. |
| Approach: | They propose a publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. |
| Outcome: | The proposed dataset achieves 31.87% accuracy on labeling a claim accompanied by the correct evidence, compared to 50.91% if we ignore the evidence. |
SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification (2022.coling-1)
Copied to clipboard
| Challenge: | Existing graph-based methods for fact verification use semantic graphs, which are based on evidence sentences. |
| Approach: | They propose to use semantic-level graph reasoning to inject its reasoning-enhanced representation into other graph-based and sequence-based reasoning methods. |
| Outcome: | The proposed method outperforms the previous graph-based methods and achieves state-of-the-art performance on a large-scale dataset for Fact Extraction and VERification (FEVER). |
An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters (2023.eacl-main)
Copied to clipboard
| Challenge: | Medical doctors spend 52 to 102 minutes per day writing clinical notes from patient encounters. |
| Approach: | They propose to use a new dataset to generate automated and manual clinical notes from doctor-patient conversations in a clinical setting. |
| Outcome: | The proposed model could reduce the time spent writing clinical notes from doctor-patient conversations in a clinical setting. |
A Multi-Level Attention Model for Evidence-Based Fact Checking (2021.findings-acl)
Copied to clipboard
| 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. |
Evaluating adversarial attacks against multiple fact verification systems (D19-1)
Copied to clipboard
| Challenge: | Automated fact verification is progressing due to advances in modeling and availability of large datasets. |
| Approach: | They propose two scoring metrics which take into account the correctness of adversarial instances. |
| Outcome: | The proposed method and paraphrasing method have higher potency and higher resilience than baselines. |
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking (2020.acl-main)
Copied to clipboard
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. |
| Approach: | They propose a system that is resilient to attacks by multiple propositions, temporal reasoning, ambiguity and lexical variation and a sequence of evidence sentences and veracity relation predictions. |
| Outcome: | The proposed system is resilient to three realistic “attacks” and obtains state-of-the-art results due to improved evidence retrieval. |