| Challenge: | Recent question generation approaches assume that the answer is known . however, such passages are what is being sought when verifying a claim. |
| Approach: | They propose a method that generates questions based on different focal points within a claim . they demonstrate that the method generates more relevant and informative questions . |
| Outcome: | The proposed method outperforms previous work on a fact-checking question generation dataset on measurable evaluation metrics. |
Similar Papers
Generating Fact Checking Explanations (2020.acl-main)
Copied to clipboard
| Challenge: | Existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. |
| Approach: | They propose to combine the generation of justifications for verdicts on claims with the multi-task model to optimize both objectives at the same time rather than training them separately. |
| Outcome: | The proposed model improves the informativeness, coverage and overall quality of the generated explanations, rather than training them separately. |
Generating Literal and Implied Subquestions to Fact-check Complex Claims (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing fact-checking systems are not reliable because it is unclear which parts of a claim are true and which are not. |
| Approach: | They propose to decompose a political claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. |
| Outcome: | The proposed models can decompose a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. |
Leveraging Context Information for Natural Question Generation (N18-2)
Copied to clipboard
| Challenge: | Existing work for natural question generation ignores the input passage or hard-codes answer positions. |
| Approach: | They propose a model that matches the answer with the passage before generating a question. |
| Outcome: | The proposed model outperforms the state-of-the-art model using rich features. |
MAGIC: Multi-Argument Generation with Self-Refinement for Domain Generalization in Automatic Fact-Checking (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for fact-checking are limited in retrieving evidence from documents . retrieved evidence derived from different sources strains generalization capabilities of classifiers . |
| Approach: | They propose a framework for cross-domain fact-checking using multi-argument generation . they propose to reconstruct concise evidence from large amounts of evidence retrieved from different sources . |
| Outcome: | The proposed framework is effective in identifying the veracity of out-of-domain claims . it can be used to extract evidence from documents and verify claims across domains . |
Ask To The Point: Open-Domain Entity-Centric Question Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | *entity-centric question generation (ECQG) is a task motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. |
| Approach: | They propose a PLM-based framework GenCONE with two modules: content focusing and question verification. |
| Outcome: | The proposed framework outperforms baselines and is effective and complementary in generating high-quality questions. |
Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing models that generate clarification questions fail to identify useful information in contexts . human ability to generate fluent and relevant questions is important in reducing ambiguity . |
| Approach: | They propose a model that first identifies what is missing and then generates a question about it. |
| Outcome: | The proposed model outperforms baselines as judged by automatic metrics and humans. |
FaVIQ: FAct Verification from Information-seeking Questions (2022.acl-long)
Copied to clipboard
| Challenge: | Existing fact verification datasets with crowdsourced claims introduce subtle biases that are difficult to control for. |
| Approach: | They construct a large-scale fact verification dataset with ambiguous questions . they use a corpus of 188k claims to construct false and true claims . |
| Outcome: | The proposed dataset outperforms models trained on the dataset FEVER or in-domain data by up to 17% absolute. |
CONSISTENT: Open-Ended Question Generation From News Articles (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts. |
| Approach: | They propose an end-to-end system for generating openended questions that are answerable from and faithful to the input text. |
| Outcome: | The proposed model outperforms existing models and can be used in news media organizations. |
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing metrics for assessing question generation fail to take into account the input context of generation. |
| Approach: | They propose a context-aware Relevance evaluation metric for Question Generation that takes into account the context of question generation into account. |
| Outcome: | The proposed metric achieves higher correlation with human judgments while being much more robust to adversarial samples. |
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims (D19-1)
Copied to clipboard
Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, Jakob Grue Simonsen
| Challenge: | Existing efforts to verify factual claims are limited by small datasets or artificially constructed datasets. |
| Approach: | They propose to use the largest publicly available dataset of naturally occurring factual claims for automatic claim verification. |
| Outcome: | The proposed model outperforms baseline models and evidence pages significantly. |