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Double-Blind Peer-Reviewing and Inclusiveness in Russian NLP Conferences

P. 3–8.
Kutuzov A. B., Никишина И. А.

Double-blind peer reviewing has been proved to be a pretty effective and fair way of academic work selection. However, to the best of our knowledge, nobody has yet analysed the effects caused by its introduction at the Russian NLP conferences. We investigate how the double-blind peer reviewing influences gender and location (according to authors’ affiliations) biases and whether it makes two of the conferences under analysis more inclusive. The results show that gender distribution has become more equal for the Dialogue conference, but did not change for the AIST conference. The authors’ location distribution (roughly divided into ‘central’ and ‘not central’) has become more equal for AIST, but, interestingly, less equal for Dialogue.

Language: English
DOI
Text on another site
Keywords: автоматическая обработка естественного языкаNeural Language Processing (NLP)Double-Blind Peer-ReviewingNLP ConferencesДвойное слепое рецензирование
Publication based on the results of:
Development of Mathematical Models and Methods for Recommender Systems and Natural Language Processing (2019)

In book

Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Lecture Notes in Computer Science, Revised Selected Papers
Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Lecture Notes in Computer Science, Revised Selected Papers
Vol. 11832. , Cham: Springer, 2019.
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