UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)
Takuma Yoneda,Jeff Mitchell,Johannes Welbl,Pontus Stenetorp,Sebastian Riedel +4 more
- pp 97-102
TLDR
This system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation that achieved a FEVER score of 62.52% on the provisional test set (without additional human evaluation), and 65.41%" on the development set.Abstract:
In this paper we describe our 2nd place FEVER shared-task system that achieved a FEVER score of 62.52% on the provisional test set (without additional human evaluation), and 65.41% on the development set. Our system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation. Retrieval is performed leveraging task-specific features, and then a natural language inference model takes each of the retrieved sentences paired with the claimed fact. The resulting predictions are aggregated across retrieved sentences with a Multi-Layer Perceptron, and re-ranked corresponding to the final prediction.read more
Citations
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Journal ArticleDOI
Survey of Hallucination in Natural Language Generation
Ziwei Ji,Nayeon Lee,Rita Frieske,Tiezheng Yu,D. Su,Yan Xu,Etsuko Ishii,Yejin Bang,Wenliang Dai,Andrea Madotto,Pascale Fung +10 more
TL;DR: This survey serves tofacilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG by providing a broad overview of the research progress and challenges in the hallucination problem inNLG.
Journal ArticleDOI
Combining Fact Extraction and Verification with Neural Semantic Matching Networks
TL;DR: Li et al. as mentioned in this paper presented a connected system consisting of three homogeneous neural semantic matching models that conduct document retrieval, sentence selection, and claim verification jointly for fact extraction and verification.
Proceedings ArticleDOI
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
TL;DR: This work proposes a simple yet effective pipeline system with special consideration on hierarchical semantic retrieval at both paragraph and sentence level, and their potential effects on the downstream task, and illustrates that intermediate semantic retrieval modules are vital for shaping upstream data distribution and providing better data for downstream modeling.
Proceedings ArticleDOI
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification
TL;DR: A graph-based evidence aggregating and reasoning (GEAR) framework which enables information to transfer on a fully-connected evidence graph and then utilizes different aggregators to collect multi-evidence information is proposed.
Proceedings ArticleDOI
Fine-grained Fact Verification with Kernel Graph Attention Network
TL;DR: Kernel Graph Attention Network (KGAT) as mentioned in this paper introduces node kernels, which better measure the importance of the evidence node, and edge kernels to conduct fine-grained evidence propagation in the graph, for more accurate fact verification.
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