Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Size:
10K - 100K
ArXiv:
Tags:
causal-reasoning
License:
Update README.md
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README.md
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@@ -134,15 +134,21 @@ There is an URL, a title, and a timestamp for each of the two headlines in every
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#### Annotation process
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#### Who are the annotators?
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### Personal and Sensitive Information
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## Considerations for Using the Data
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#### Annotation process
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Every candidate pair was annotated with [Yandex Toloka](https://toloka.ai/), a crowdsourcing platform. The task was to determine a relationship between two headlines, A and B. There were seven possible options: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is not linked to B. An annotation guideline was in Russian for Russian news and in English for English news.
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Guidelines:
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* Russian: [link](https://ilyagusev.github.io/HeadlineCause/toloka/ru/instruction.html)
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* English: [link](https://ilyagusev.github.io/HeadlineCause/toloka/en/instruction.html)
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Ten workers annotated every pair. The total annotation budget was 870$, with the estimated hourly wage paid to participants of 45 cents. Annotation management was semi-automatic. Scripts are available in the [Github repository](https://github.com/IlyaGusev/HeadlineCause).
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#### Who are the annotators?
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Yandex Toloka workers were the annotators, 457 workers for the Russian part, 180 workers for the English part.
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### Personal and Sensitive Information
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The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset. No information about annotators is included except a platform worker ID.
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## Considerations for Using the Data
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