skip to main content
Guest
My Research
My Account
Sign out
Sign in
This feature requires javascript
Library Search
Find Databases
Browse Search
E-Journals A-Z
E-Books A-Z
Citation Linker
Help
Language:
English
Vietnamese
This feature required javascript
This feature requires javascript
Primo Search
All Library Resources
All
Course Materials
Course Materials
Search For:
Clear Search Box
Search in:
All Library Resources
Or select another collection:
Search in:
All Library Resources
Search in:
Print Resources
Search in:
Digital Resources
Search in:
Online E-Resources
Advanced Search
Browse Search
This feature requires javascript
Search Limited to:
Search Limited to:
Resource type
criteria input
All items
Books
Articles
Images
Audio Visual
Maps
Graduate theses
Show Results with:
criteria input
that contain my query words
with my exact phrase
starts with
Show Results with:
Search type Index
criteria input
anywhere in the record
in the title
as author/creator
in subject
Full Text
ISBN
ISSN
TOC
Keyword
Field
Show Results with:
in the title
Show Results with:
anywhere in the record
in the title
as author/creator
in subject
Full Text
ISBN
ISSN
TOC
Keyword
Field
This feature requires javascript
Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning
info:eu-repo/semantics/OpenAccess ;ISSN: 1076-9757 ;EISSN: 1943-5037
Full text available
View Online
Details
Recommendations
Reviews
External Links
This feature requires javascript
Actions
Add to My Research
Remove from My Research
E-mail
Print
Permalink
Citation
EasyBib
EndNote
RefWorks
Delicious
Export RIS
Export BibTeX
This feature requires javascript
Title:
Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning
Author:
Erdem, E
;
Kuyu, M
;
Yagcioglu, S
;
Frank, A
;
Pârcălăbescu, L
;
Plank, B
;
Babii, A
;
Turuta, O
;
Erdem, A
;
Calixto, I
;
Lloret, E
;
Elena-Apostol, S
;
Ciprian-Truică, O
;
Š
;
rih, B
;
Martinčić-Ipšic, S
;
Berend, G
;
Gatt, A
;
Korvel, G
Subjects:
natural
language
;
neural networks
Description:
Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.
Creation Date:
2022-04
Language:
English
Identifier:
ISSN: 1076-9757
EISSN: 1943-5037
Source:
Open Access: Freely Accessible Journals by multiple vendors
Alma/SFX Local Collection
ProQuest Central
DOAJ Directory of Open Access Journals
American Association for Artificial Intelligence publications
Utrecht University Repository
This feature requires javascript
This feature requires javascript
Back to results list
This feature requires javascript
This feature requires javascript
Searching Remote Databases, Please Wait
Searching for
in
scope:(TDTS)
Show me what you have so far
This feature requires javascript
This feature requires javascript