A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 76978998
- Type
- D - Journal article
- DOI
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10.1371/journal.pone.0140866
- Title of journal
- PLoS ONE
- Article number
- 084001
- First page
- -
- Volume
- 10
- Issue
- 11
- ISSN
- 1932-6203
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
A - Computer Science
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Novel, large-scale neural computation model of language learning.
This model has been extended in the Best Paper Award at the 17th SoMeT Conference in Granada 2018 (Jorgji, Golosio, Cangelosi & Masala, ANNABELL, a cognitive system able to learn different languages), showing its suitability for learning multi-language dialog systems.
Media:
- The Times (Nov 2015) and Daily Mail (Jan 2015)
- Numerous international media (e.g. Discover Magazine, La Repubblica newspaper, IndiaToday) and online news websites (e.g. Wired.co.uk, ScienceDaily, NeurosienceNews, LanguageMagazine).
The work has received over 60,000 views (as per 2020) in the journal website."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
