APRIL : Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning. / Gao, Yang; Meyer, Christian M.; Gurevych, Iryna.

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium : Association for Computational Linguistics, 2018. p. 4120-4130.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

APRIL : Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning. / Gao, Yang; Meyer, Christian M.; Gurevych, Iryna.

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium : Association for Computational Linguistics, 2018. p. 4120-4130.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Gao, Y, Meyer, CM & Gurevych, I 2018, APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning. in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, pp. 4120-4130. https://doi.org/10.18653/v1/D18-1445

APA

Gao, Y., Meyer, C. M., & Gurevych, I. (2018). APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 4120-4130). Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1445

Vancouver

Gao Y, Meyer CM, Gurevych I. APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics. 2018. p. 4120-4130 https://doi.org/10.18653/v1/D18-1445

Author

Gao, Yang ; Meyer, Christian M. ; Gurevych, Iryna. / APRIL : Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium : Association for Computational Linguistics, 2018. pp. 4120-4130

BibTeX

@inproceedings{aba954d6883f4f66bd905e420260d06e,
title = "APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning",
abstract = "We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users{\textquoteright} preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/emnlp2018-april.",
author = "Yang Gao and Meyer, {Christian M.} and Iryna Gurevych",
year = "2018",
month = oct,
day = "31",
doi = "10.18653/v1/D18-1445",
language = "English",
pages = "4120--4130",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
publisher = "Association for Computational Linguistics",

}

RIS

TY - GEN

T1 - APRIL

T2 - Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning

AU - Gao, Yang

AU - Meyer, Christian M.

AU - Gurevych, Iryna

PY - 2018/10/31

Y1 - 2018/10/31

N2 - We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users’ preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/emnlp2018-april.

AB - We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users’ preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/emnlp2018-april.

U2 - 10.18653/v1/D18-1445

DO - 10.18653/v1/D18-1445

M3 - Conference contribution

SP - 4120

EP - 4130

BT - Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

PB - Association for Computational Linguistics

CY - Brussels, Belgium

ER -