使用異常偵測原理進行事實查核

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姓名

紀滎姿(In-Zu Gi) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

使用異常偵測原理進行事實查核
(Fact Verification Using Out of distribution Detection)

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至系統瀏覽論文 (2025-12-31以後開放)

摘要(中)

社群媒體包含多樣且未經過濾的訊息，為了避免傳播未經證實的主張，這使得自動事實查核被迫切需要。自動事實查核旨在考慮特定證據的狀況下，決定主張之真實性。現有方法通常遵循兩種方法，包括基於蘊涵的三元分類方法或典型多類別分類等設置，這兩種方法對於真實情況都有其不足之處。在本文中，我們提出了一種新穎的模型，據我們所知，這是第一個利用異常偵測原理進行事實查核的模型。我們為異常實例設計額外的損失函數，並且成功地使特定類別的學習更加專注。實驗結果顯示我們提出的模型可以表現更佳或具有競爭力的結果，這證明了在兩個基準數據集，FEVEROUS 和 LIAR 資料集上的有效性。

摘要(英)

Social media contains diverse and unfiltered information, which makes automatic fact verification in urgent need against the dissemination of unproven claims. Fact Verification aims to assign an authenticity verdict to a claim in particular evidence. Existing approaches usually follow the setting of entailment-based ternary or typical multi-class classification, which are not feasible for realistic scenarios. In this paper, we propose a novel model, which is, to our best knowledge, the first to exploit out-of-distribution detection for fact checking. By incorporating an extra keep-away loss for out-of-distribution instances, we successfully engage the learning with respect to specific categories. Experimental results show our model performs better or competitive results, which demonstrates the effectiveness on two benchmark datasets, the FEVEROUS and LIAR dataset.

關鍵字(中)

★ 事實查核
★ FEVEROUS
★ 自然語言處理
★ 假新聞

關鍵字(英)

★ Fact Verification
★ FEVEROUS
★ Natural Language Processing
★ Fake News

論文目次

摘要 i
ABSTRACT ii
致謝 iii
目錄 iv
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
Chapter 2 Related Work 6
2.1 Fact Verification 6
2.2 Out-of-Distribution Detection 8
Chapter 3 Problem Statement 11
Chapter 4 Our Model 12
4.1 Semantic Encoder 12
4.2 Out-of-Distribution Aware Network 13
Chapter 5 Experiment Setting 15
5.1 Dataset 15
5.2 Evaluation Metric 17
5.3 Baseline 18
5.4 Implementation Details 19
Chapter 6 Experiments 21
6.1 Experimental Results 21
6.2 Ablation Analysis 23
6.3 Visualizations 24
6.4 Qualitative Analysis 26
Chapter 7 Conclusion 29
References 30

參考文獻

Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018. Where is your evidence: Improving factchecking by justification modeling. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 85–90, Brussels, Belgium. Association for Computational Linguistics.

Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, and Arpit Mittal. 2021a. Feverous: Fact extraction and verification over unstructured and structured information. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1.

Rami Aly, Zhijiang Guo, Michael Sejr Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, and Arpit Mittal. 2021b. The fact extraction and VERification over unstructured and structured information (FEVEROUS) shared task. In Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER), pages 1–13, Dominican Republic. Association
for Computational Linguistics.

Mostafa Bouziane, Hugo Perrin, Amine Sadeq, Thanh Nguyen, Aurélien Cluzeau, and Julien Mardas. 2021. FaBULOUS: Fact-checking based on understanding of language over unstructured and structured information. In Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER), pages 31–39, Dominican Republic. Association for Computational Linguistics.

In-Zu Gi, Ting-Yu Fang, and Richard Tzong-Han Tsai. 2021. Verdict inference with claim and retrieved elements using RoBERTa. In Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER), pages 60–65, Dominican Republic. Association for Computational Linguistics.

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Jiasheng Si, Deyu Zhou, Tongzhe Li, Xingyu Shi, and Yulan He. 2021. Topic-aware evidence reasoning and stance-aware aggregation for fact verification. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1612–1622, Online. Association for Computational Linguistics.

Andreas Vlachos and Sebastian Riedel. 2014. Fact checking: Task definition and dataset construction. In Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pages 18–22, Baltimore, MD, USA. Association for Computational Linguistics.

William Yang Wang. 2017. “liar, liar pants on fire”: A new benchmark dataset for fake news detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 422–426, Vancouver, Canada. Association for Computational Linguistics.

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指導教授

蔡宗翰(Richard Tzong-Han Tsai)

審核日期

2022-9-23

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