| 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. |
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QA-NatVer: Question Answering for Natural Logic-based Fact Verification (2023.emnlp-main)
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| Challenge: | Recent work has focused on natural logic, which operates directly on natural language by capturing the semantic relation of spans between an aligned claim and its evidence via set-theoretic operators. |
| Approach: | They propose to use question answering to predict natural logic operators using generalization capabilities of instruction-tuned language models. |
| Outcome: | The proposed approach outperforms the best baseline on a Danish verification dataset by 4.3 accuracy points. |
AnswerFact: Fact Checking in Product Question Answering (2020.emnlp-main)
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| Challenge: | a product-related community question answering platform is widely employed in many E-commerce sites . however, the misinformation in the answers on those platforms poses unprecedented challenges for users to obtain reliable and truthful product information. |
| Approach: | They propose a large scale fact checking dataset from product question answering forums to predict the answer veracity . each answer is accompanied by its veraity label and associated evidence sentences . |
| Outcome: | The proposed model outperforms baselines on the question veracity prediction task. |
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. |
| Approach: | They propose to use a table-based language model to verify textual statements . they use cell embeddings and numerical information to improve accuracy . |
| Outcome: | The proposed method outperforms the state-of-the-art model on question answering tasks even without modeling numerical information. |
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 . |
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases (C18-1)
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| Challenge: | Simple question answering over knowledge bases is one of the most important natural language processing tasks. |
| Approach: | They propose to conduct pattern extraction and entity linking first and put forward pattern revising procedure to mitigate the error propagation problem. |
| Outcome: | The proposed method outperforms the current state-of-the-art in this task by an absolute large margin. |
Unsupervised Question Answering by Cloze Translation (P19-1)
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| Challenge: | Existing QA datasets only available for limited domains and languages. |
| Approach: | They propose to generate context, question and answer triples in an unsupervised manner and synthesize extractive QA training data automatically. |
| Outcome: | The proposed approach outperforms existing QA models on a common EQA benchmark dataset. |
Unsupervised FAQ Retrieval with Question Generation and BERT (2020.acl-main)
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| Challenge: | Frequently Asked Questions (FAQ) retrieval requires labeled datasets for training neural models. |
| Approach: | They propose to exploit FAQ pairs to train two BERT models that match user queries to FAQ answers and questions. |
| Outcome: | The proposed model outperforms supervised models on existing datasets and is on par with existing dataset. |
Constrained Fact Verification for FEVER (2020.emnlp-main)
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| Challenge: | Existing methods for fact verification rely on extracted evidence, but there is little work on understanding the reasoning process. |
| Approach: | They propose a method that enforces a closed-world reliance on extracted evidence to verify a claim's factuality. |
| Outcome: | The proposed model outperforms existing models on the FEVER shared task and shows that it is more accurate than previous models. |
FEVER: a Large-scale Dataset for Fact Extraction and VERification (N18-1)
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| 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. |
Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task (D19-66)
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| Challenge: | Existing methods of fact checking are based on the assignment of a truth value to a given (factual) statement, and therefore it is desirable to have access to the evidence used to reach an assignment. |
| Approach: | They propose a two-staged sentence selection strategy to account for examples in the dataset where evidence is not only conditioned on the claim, but also on previously retrieved evidence. |
| Outcome: | The proposed system beats the top performing systems of the first FEVER challenge which act as a baseline, beating 64.21% of the top-performing systems. |