Low-Shot Learning for Fictional Claim Verification
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Abstract (arXiv)
In this paper, we study the problem of claim verification in the context of claims about fictional stories in a low-shot learning setting. To this end, we generate two synthetic datasets and then develop an end-to-end pipeline and model that is tested on both benchmarks. To test the efficacy of our pipeline and the difficulty of benchmarks, we compare our models' results against human and random assignment results. Our code is available at https://github.com/Derposoft/plot_hole_detection.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 5 Apr 2023.
Builds on
11 of 33 references.
References · 11
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2017Graph Attention Networkscited by 8,923
- 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
- 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,059
- 2021How Attentive are Graph Attention Networks?cited by 835
- 2020Fact or Fiction: Verifying Scientific Claimscited by 554
- 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 546
- 2020DeeperGCN: All You Need to Train Deeper GCNscited by 250
- 2021COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemiccited by 100
- 2021Claim Matching Beyond English to Scale Global Fact-Checkingcited by 42
- 2018A Simple and Effective Approach to the Story Cloze Testcited by 10
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