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Large-Scale Transfer Learning for Natural Language Generation - 2019

Research Area:  Machine Learning

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.

Author(s) Name:  Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alexander Tselousov, Thomas Wolf

Journal name:  Association for Computational Linguistics

Conferrence name:  

Publisher name:  ACL

DOI:  10.18653/v1/P19-1608

Volume Information: