Papers with fine-grained labeling
Annotation-Scheme Reconstruction for “Fake News” and Japanese Fake News Dataset (2022.lrec-1)
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| Challenge: | Contemporary research focuses on the factuality aspect of the news, but this aspect alone is insufficient to explain “fake news.” |
| Approach: | They propose to use Japanese fake news datasets to classify whether news content is false . they propose to do this by using existing fake news data to investigate fake news . |
| Outcome: | The proposed scheme will provide an in-depth understanding of fake news in Japan and other languages. |
EmoS: A High-Fidelity Multimodal Benchmark for Fine-grained Streaming Emotional Understanding (2026.acl-long)
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| Challenge: | Existing benchmarks fail to achieve ecological validity, signal clarity, and reliable fine-grained labeling in multimodal Emotion Recognition (MER) Existing datasets lack spontaneity of real-life interactions, resulting in poor quality and inconsistent data quality. |
| Approach: | They propose a bilingual benchmark to resolve limitations of ecological validity and noise in existing datasets by combining strictly filtered static slices with a dynamic Streaming Monologue subset. |
| Outcome: | EmoS provides trusted ground truth that captures continuous emotional evolution. |