A Repository of Conversational Datasets
This provides a resource for researchers in conversational AI to benchmark and improve models, though it is incremental as it focuses on data collection and standardization rather than novel methods.
The authors tackled the lack of large, standardized conversational datasets by creating a repository with hundreds of millions of examples and a standardized evaluation procedure using '1-of-100 accuracy', and they introduced competitive baselines and a neural encoder model trained on the full dataset.
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using '1-of-100 accuracy'. The repository contains scripts that allow researchers to reproduce the standard datasets, or to adapt the pre-processing and data filtering steps to their needs. We introduce and evaluate several competitive baselines for conversational response selection, whose implementations are shared in the repository, as well as a neural encoder model that is trained on the entire training set.