mlsanityData to 7 Oct 2026

Towards transparency in NLP shared tasks

arXiv 2105.05020 ↗First posted 11 May 2021cs.CLcited by 3

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Abstract (arXiv)

This article reports on a survey carried out across the Natural Language Processing (NLP) community. The survey aimed to capture the opinions of the research community on issues surrounding shared tasks, with respect to both participation and organisation. Amongst the 175 responses received, both positive and negative observations were made. We carried out and report on an extensive analysis of these responses, which leads us to propose a Shared Task Organisation Checklist that could support future participants and organisers. The proposed Checklist is flexible enough to accommodate the wide diversity of shared tasks in our field and its goal is not to be prescriptive, but rather to serve as a tool that encourages shared task organisers to foreground ethical behaviour, beginning with the common issues that the 175 respondents deemed important. Its usage would not only serve as an instrument to reflect on important aspects of shared tasks, but would also promote increased transparency around them.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 30 references that are in the map. First posted on arXiv 11 May 2021.

Builds on

3 of 30 references.

References · 3

  1. 2019Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challengecited by 86
  2. 2018Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequencescited by 36
  3. 2018Findings of the E2E NLG Challengecited by 28

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Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.