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Combining Fact Extraction and Verification with Neural Semantic Matching Networks Yixin Nie, Haonan Chen, Mohit Bansal Background and Motivation Authentic Statement Fake Statement 2 Background and Motivation Authentic Statement Fake

  1. Combining Fact Extraction and Verification with Neural Semantic Matching Networks Yixin Nie, Haonan Chen, Mohit Bansal

  2. Background and Motivation Authentic Statement Fake Statement 2

  3. Background and Motivation Authentic Statement Fake Statement 3

  4. Background and Motivation Our Goal Authentic Statement How to discriminate between truth and falsehood ? Fake Statement 4

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sha1_base64="7/CGXDZm+jc+hQ8EQBrQPIRvFW8=">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</latexit> Task and Dataset [Thorne et al, NAACL 2018] Task Formalization: Input: c (claim); P (evidence set) y (predicted label); ˆ Output: ˆ E (predicted evidence set) Evaluation: y, E ⊆ ˆ y = ˆ E 5

  6. Task and Dataset [Thorne et al, NAACL 2018] 3 Subtasks: (1)Document Retrieval (2)Sentence Selection (3)Claim Verification 6

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A Fine-grained and Noise-aware Method for Neural Relation Extraction ADVISOR: JIA-LING, KOH

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1 A Fine-grained and Noise-aware Method for Neural Relation Extraction ADVISOR: JIA-LING, KOH SOURCE: CIKM 2019 SPEAKER: SHAO-WEI, HUANG DATE: 2020/05/04 2 OUTLINE Introduction Method Experiment Conclusion 3 INTRODUCTION

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