We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| ukplab/openreview-licensing-workflow | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| escape_venue_file_name | Ran | ukplab/openreview-licensing-workflow/yyy/collect.py code served (permissive licence) · get_code("f7e1cc04893f0825") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The shift towards publicly available text sources has enabled language processing at unprecedented scale, yet leaves under-serviced the domains where public and openly licensed data is scarce. Proactively collecting text data for research is a viable strategy to address this scarcity, but lacks systematic methodology taking into account the many ethical, legal and confidentiality-related aspects of data collection. Our work presents a case study on proactive data collection in peer review -- a challenging and under-resourced NLP domain. We outline ethical and legal desiderata for proactive data collection and introduce "Yes-Yes-Yes", the first donation-based peer reviewing data collection workflow that meets these requirements. We report on the implementation of Yes-Yes-Yes at ACL Rolling Review and empirically study the implications of proactive data collection for the dataset size and the biases induced by the donation behavior on the peer reviewing platform.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2201.11443")
get_code_for_paper("2201.11443")
have("2201.11443")
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