Papers with fact selection

3 papers
Simple Question Answering with Subgraph Ranking and Joint-Scoring (N19-1)

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Challenge: Knowledge graph based simple question answering is a major area of research in question answering.
Approach: They propose a framework to describe and analyze existing knowledge graph based simple question answering approaches.
Outcome: The proposed model achieves a state-of-the-art (85.44% accuracy) on the SimpleQuestions dataset.
FaiRR: Faithful and Robust Deductive Reasoning over Natural Language (2022.acl-long)

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Challenge: Currently, black-box models generate both the proof graph and intermediate inferences within the same model and thus may be unfaithful.
Approach: They propose a transformer-based model that can perform deductive reasoning on a logical rulebase containing rules and statements written in natural language.
Outcome: The proposed model is robust to language perturbations and faster at inference than previous models on existing reasoning datasets.
FRVA: Fact-Retrieval and Verification Augmented Entailment Tree Generation for Explainable Question Answering (2024.findings-acl)

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Challenge: Existing methods for generating a entailment tree exhibit the reasoning chains from knowledge facts to predicted answers, but they have large fact search spaces and error accumulation problems resulting in the generation of invalid steps.
Approach: They propose a Fact-Retrieval and Verification Augmented bidirectional entailment tree generation method that contains two systems.
Outcome: The proposed method outperforms existing models and achieves state-of-the-art performance in fact selection and structural correctness.

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