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Responsible NLP Research Checklist
Members of the ACL are responsible for adhering to the ACL code of ethics. The ARR Responsible NLP Research checklist is designed to encourage best practices for responsible research, addressing issues of research ethics, societal impact and reproducibility.
Please read the Responsible NLP Research checklist guidelines for information on how to answer these questions. Note that not answering positively to a question is not grounds for rejection.
All supporting evidence can appear either in the main paper or the supplemental material. For each question, if you answer Yes, provide the section number; if you answer No, provide a justification.
Please do not modify, reorder, delete or add questions, question options or other wording of this document.
A For every submission
A1 Did you discuss the limitations of your work?
If you answer Yes, provide the section number; if you answer No, provide a justification.
Section or justification Section 5.4, Section 6
A2 Did you discuss any potential risks of your work?
Section or justification Click or tap here to enter text.
A3 Do the abstract and introduction summarize the paper's main claims?
Section or justification Abstract, Section 1
B Did you use or create scientific artifacts?
If you answer Yes, provide the section number; if you answer No, you can skip the rest of this section.
If yes:
B1 Did you cite the creators of artifacts you used?
Section or justification Section 4.1
B2 Did you discuss the license or terms for use and/or distribution of any artifacts?
Section or justification Section 1
B3 Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)?
B4 Did you discuss the steps taken to check whether the data that was collected/used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it?
Section or justification They are widely used datasets containing commonsense/scientific information.
B5 Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.?
B6 Did you report relevant statistics like the number of examples, details of train/test/dev splits, etc. for the data that you used/created?
C Did you run computational experiments?
C1 Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used?
Section or justification Section 4.2, Section 5.5
C2 Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values?
Section or justification Section 4.2
C3 Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run?
Section or justification Section 4.2, Section 5
C4 If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)?
D Did you use human annotators (e.g., crowdworkers) or research with human subjects?
D1 Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.?
Section or justification Section 5.4
D2 Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)?
D3 Did you discuss whether and how consent was obtained from people whose data you're using/curating (e.g., did your instructions explain how the data would be used)?
D4 Was the data collection protocol approved (or determined exempt) by an ethics review board?
D5 Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data?