Predicting User Engagement Status for Online Evaluation of Intelligent Assistants.
Rui Meng|Zhen Yue|Alyssa Glass
| Anthology ID: | DBLP:conf/ecir/MengYG21 |
|---|---|
| Volume: | Advances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28 - April 1, 2021, Proceedings, Part I |
| Year: | 2021 |
| Venue: | European Conference on Advances in Information Retrieval (ECIR) |
| Publisher: | Springer |
| Pages: | 433-450 |
| URL: | https://doi.org/10.1007/978-3-030-72113-8_29 |
| DOI: | https://doi.org/10.1007/978-3-030-72113-8_29 |
| DBLP: | conf/ecir/MengYG21 |
| BibTeX: |
@inproceedings{meng-2021-predicting,
author = {Rui Meng and
Zhen Yue and
Alyssa Glass},
editor = {Djoerd Hiemstra and
Marie-Francine Moens and
Josiane Mothe and
Raffaele Perego and
Martin Potthast and
Fabrizio Sebastiani},
title = {{Predicting User Engagement Status for Online Evaluation of Intelligent Assistants}},
booktitle = {{Advances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28 - April 1, 2021, Proceedings, Part I}},
series = {Lecture Notes in Computer Science},
volume = {12656},
pages = {433--450},
publisher = {Springer},
year = {2021},
url = {https://doi.org/10.1007/978-3-030-72113-8_29},
doi = {https://doi.org/10.1007/978-3-030-72113-8_29}
}