posted by user: grupocole || 3775 views || tracked by 2 users: [display]

DeeLIO 2020 : Deep Learning Inside Out

FacebookTwitterLinkedInGoogle

Link: https://sites.google.com/view/deelio-ws/
 
When Nov 11, 2020 - Nov 12, 2020
Where Punta Cana, Dominican Republic
Submission Deadline Jan 15, 2020
Notification Due Aug 17, 2020
Final Version Due Aug 31, 2020
Categories    NLP
 

Call For Papers

*** DeeLIO - First Call for Papers ***

Deep Learning Inside Out (DeeLIO)
The First Workshop on Knowledge Extraction and Integration for Deep Learning
Architectures
https://sites.google.com/view/deelio-ws/
Collocated with EMNLP 2020 in Punta Cana, Dominican Republic.


Workshop description
---------------------------------------

Deep learning methods have opened up a new era in NLP, providing the
community with extremely powerful tools and language representations, and
reaching impressive performance in numerous tasks. After the first
enthusiasm this success stirred, the community started looking inside the
box to understand what is coded in there, but also outside of the same
(neural) box, seeking other potentially useful sources of language-related
information. Deep Learning Inside Out (DeeLIO) is the first workshop on
knowledge extraction and integration for deep learning architectures. It
aims to bring together the knowledge interpretation, extraction and
integration lines of research in deep learning, and cover the area in
between. It will explore the introduction of external knowledge in deep
learning models and representations, the types of linguistic and real-world
knowledge neural nets encode, the extent to which this can be used for
building resources, and whether this knowledge can be beneficial to them by
being re-integrated in the models, compared to external hand-crafted
knowledge. DeeLIO also has a strong focus on structurally diverse languages
with varying semantic-syntactic properties and low-data regimes. The
workshop’s aim is to inspire novel variation-aware transfer learning and
multilingual solutions on how to use the knowledge from resource-rich
languages to inform deep learning architectures where external repositories
are scarce or missing. The focus is on lexico-semantic knowledge that can be
recovered from, or integrated into, deep learning methods across a variety
of languages.

Topics of interest include but are not limited to:

* integration of external knowledge in neural networks (under the form of
semantic specialization of embeddings, retrofitting, joint modeling, or
other);
* exploration of the types of linguistic and world knowledge neural models,
architectures and representations encode;
* extraction of linguistic and world knowledge from deep learning models;
* use of the knowledge extracted from deep learning models in practice (for
resource enrichment, knowledge transfer to resource-lean languages, or
other);
* analysing and understanding the limitations of the knowledge about
language and the world acquired by current neural models;
* probing and analysing different types of hand-crafted knowledge that can
enhance “blind” distributional models;
* benefits of using external versus internally encoded knowledge, and their
combination, for knowledge enhancement in neural networks;
* development and enrichment of lexico-semantic knowledge resources using
deep learning models;
* (re)integration of (semi-)automatically compiled resources into deep
learning models;
* using external knowledge in resource-lean languages through transfer
techniques or joint multilingual modelling.


Submission
----------------------
We invite the submission of long and short papers on original and
unpublished research in any topic related with knowledge interpretation and
integration in deep neural networks. Long papers may consist of up to 8
pages of content + references. Short papers may consist of up to 4 pages of
content + references. Upon acceptance, both types of papers will be given
one additional page of content. Authors are encouraged to use this
additional page for addressing reviewers’ comments in the final version.
Paper submission is electronic, using the Softconf START conference
management system. Paper template files will be provided soon on the
workshop website.
Double submission of papers will need to be notified at submission.

The DeeLIO workshop will be collocated with EMNLP 2020 in Punta Cana,
Dominican Republic.


Important Dates
------------------------------
• Deadline for submission: July 15, 2020
• Notification of Acceptance: August 17, 2020
• Camera-ready papers due: August 31, 2020
• Workshop: November 11 or 12, 2020

Note: All deadlines are 11:59PM UTC-12:00.


Workshop Chairs
-------------------------------
Eneko Agirre (University of the Basque Country)
Marianna Apidianaki (University of Helsinki)
Ivan Vulić (University of Cambridge)

Related Resources

MLDM 2023   18th International Conference on Machine Learning and Data Mining
CBW 2023   4th International Conference on Cloud, Big Data and Web Services
DLTCI 2023   Special Issue on: Deep Learning Techniques for Cancer Imaging
IEEE Big Data - MMBD 2022   IEEE Big Data 2022 Workshop on Multimodal Big Data (Virtually)
Current IoT Trends 2023   Current IoT Trends, Issues, and Future Potential Using AI & Machine Learning Techniques
NLPCL 2023   4th International Conference on Natural Language Processing and Computational Linguistics
MDM special issue 2022   MDM special issue: Diagnosis of early Alzheimer's disease based on Artificial Intelligence
NCO 2023   9th International Conference on Networks and Communications
Trustworthy Artificial Intelligence 2023   Trustworthy Artificial Intelligence for Big Data-Driven Research Applications based on Internet of Everythings
FAIML 2023   2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2023)