Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferences
This work offers insights for NLP researchers on dataset trends, but it is incremental as it focuses on analysis rather than novel methods.
The paper analyzed trends in 92 new datasets introduced at the 2022 ACL and EMNLP conferences, highlighting that better quality datasets during pretraining improve large language model performance, and provided suggestions for future dataset curation.
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.