A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Benjamin Riedel, Isabelle Augenstein, Georgios P. Spithourakis, Sebastian Riedel
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ReproduceCode
- github.com/uclmr/fakenewschallengeOfficialIn papertf★ 0
- github.com/uclnlp/fakenewschallengetf★ 0
- github.com/pmallari/AmazonSentimentAnalysispytorch★ 0
- github.com/chimera-detector/Extensionnone★ 0
- github.com/gabrielsaruhashi/anti-faketf★ 0
- github.com/chimera-detector/Servertf★ 0
- github.com/chimera-detector/experienceExtensionnone★ 0
- github.com/ankitp544/stance_detectiontf★ 0
- github.com/harshita97/hoaxbaittf★ 0
Abstract
Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towards the assertion. Automatic stance evaluation, i.e. stance detection, would arguably facilitate the process of fact checking. In this paper, we present our stance detection system which claimed third place in Stage 1 of the Fake News Challenge. Despite our straightforward approach, our system performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our system as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance detection task.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| FNC-1 | 3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017) | Weighted Accuracy | 81.72 | — | Unverified |