We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| di-dimitrov/SEMEVAL-2021-task6-corpus | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| check_format_task1_task3 | Ran | di-dimitrov/SEMEVAL-2021-task6-corpus/format_checker/task1_3.py pointer only (licence: NONE) · get_code("225b78b6dd217d03") |
| read_classes | Ran | di-dimitrov/SEMEVAL-2021-task6-corpus/format_checker/task1_3.py pointer only (licence: NONE) · get_code("08e612db25f6cfb2") |
| evaluate | Not yet run | di-dimitrov/SEMEVAL-2021-task6-corpus/scorer/task1_3.py pointer only (licence: NONE) · get_code("7c09e8b2b1bf0d9b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) detecting the techniques in the text, (ii) detecting the text spans where the techniques are used, and (iii) detecting techniques in the entire meme, i.e., both in the text and in the image. It was a popular task, attracting 71 registrations, and 22 teams that eventually made an official submission on the test set. The evaluation results for the third subtask confirmed the importance of both modalities, the text and the image. Moreover, some teams reported benefits when not just combining the two modalities, e.g., by using early or late fusion, but rather modeling the interaction between them in a joint model.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2105.09284")
get_code_for_paper("2105.09284")
have("2105.09284")
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