Large-Scale Transfer Learning for Natural Language Generation

Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alexander Tselousov, Thomas Wolf


Abstract
Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks. We study how these architectures can be applied and adapted for natural language generation, comparing a number of architectural and training schemes. We focus in particular on open-domain dialog as a typical high entropy generation task, presenting and comparing different architectures for adapting pretrained models with state of the art results.
Anthology ID:
P19-1608
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6053–6058
Language:
URL:
https://aclanthology.org/P19-1608
DOI:
10.18653/v1/P19-1608
Bibkey:
Cite (ACL):
Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alexander Tselousov, and Thomas Wolf. 2019. Large-Scale Transfer Learning for Natural Language Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6053–6058, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Large-Scale Transfer Learning for Natural Language Generation (Golovanov et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1608.pdf