Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.18653/v1/w19-4101 | NLP FOR CONVERSATIONAL AI |
DocType | Volume | Citations |
Journal | abs/1904.06472 | 1 |
PageRank | References | Authors |
0.34 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Henderson | 1 | 158 | 8.90 |
| Pawel Budzianowski | 2 | 59 | 9.50 |
| Iñigo Casanueva | 3 | 1 | 0.34 |
| Sam Coope | 4 | 2 | 1.37 |
| Daniela Gerz | 5 | 39 | 4.68 |
| Daniela Gerz | 6 | 39 | 4.68 |
| Nikola Mrksic | 7 | 1 | 0.34 |
| Georgios P. Spithourakis | 8 | 12 | 2.07 |
| Pei-hao Su | 9 | 382 | 22.09 |
| Ivan Vulic | 10 | 462 | 52.59 |
| Tsung-Hsien Wen | 11 | 475 | 24.92 |