Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferences
Jesse Atuhurra Hidetaka Kamigaito
Natural language processing (NLP) has grown significantly since the advent of
the Transformer architecture. Transformers have given birth to pre-trained
large language models (PLMs). There has been tremendous improvement in the
performance of NLP systems across several tasks. NLP systems are on par or, in
some cases, better than humans at accomplishing specific tasks. However, it
remains the norm that \emph{better quality datasets at the time of pretraining
enable PLMs to achieve better performance, regardless of the task.} The need to
have quality datasets has prompted NLP researchers to continue creating new
datasets to satisfy particular needs. For example, the two top NLP conferences,
ACL and EMNLP, accepted ninety-two papers in 2022, introducing new datasets.
This work aims to uncover the trends and insights mined within these datasets.
Moreover, we provide valuable suggestions to researchers interested in curating
datasets in the future.