ENCONTER: Entity constrained progressive sequence generation via insertion-based transformer

ENCONTER: Entity constrained progressive sequence generation via insertion-based transformer

Pretrained using large amount of data, autoregressive language models are able to generate high quality sequences. However, these models do not perform well under hard lexical constraints as they lack fine control of content generation process. Progressive insertion-based transformers can overcome t...

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Bibliographic Details
Main Authors: HSIEH, Lee Hsun, LEE, Yang Yin, LIM, Ee-Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Databases and Information Systems
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/5891
https://ink.library.smu.edu.sg/context/sis_research/article/6894/viewcontent/1._ENCONTER__Entity_Constrained_Progressive_Sequence_Generation_viaInsertion_based_Transformer__EACL_2021.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/5891
https://ink.library.smu.edu.sg/context/sis_research/article/6894/viewcontent/1._ENCONTER__Entity_Constrained_Progressive_Sequence_Generation_viaInsertion_based_Transformer__EACL_2021.pdf

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