The 1st Workshop on Deep Learning Approaches for Low-Resource Natural
Language Processing (DeepLo) Workshop at the Annual Conference of  the
Association for Computational Linguistics (ACL), Melbourne, Australia

https://sites.google.com/view/deeplo18/home



WORKSHOP DESCRIPTION

Natural Language Processing is being revolutionized by deep learning with
neural networks. However, deep learning requires large amounts of annotated
data, and its advantage over traditional statistical methods typically
diminishes when such data is not available; for example,  SMT continues to
outperform NMT in many bilingually resource-poor scenarios. Large amounts
of annotated data do not exist for for many low-resource languages, and for
high-resource languages it can be difficult to find linguistically
annotated data of sufficient size and quality to allow neural methods to
excel.

This workshop aims to bring together researchers from the NLP and ML
communities who work on learning with neural methods when there is not
enough data for those methods to succeed out-of-the-box.



WORKSHOP TOPICS

Transfer learning

Multi-task learning

Semi-supervised learning

Dual learning

Unsupervised learning

Active learning

Generative adversarial networks

Bandit learning

Domain adaptation

Decipherment or zero-shot learning

Language projections

Universal representations and interlinguas

Low resource structured prediction



IMPORTANT DATES


All submission deadlines are at 11:59 p.m. Anywhere on Earth (UTC-12)


Paper submission: April 8, 2018 (extended deadline)

Notification of acceptance: May 7, 2018

Camera-ready submission: May 28, 2018

Workshop date: July 19, 2018



SUBMISSION

Submissions consist of up to eight pages of content; there is no limit on
the number of pages for references. Authors can also submit extended
abstracts of up to eight pages of content. Extended abstracts will not be
included in the proceedings. Thus, your work will retain the status of
being unpublished and later submission at another venue (e.g., a journal)
is not precluded. Likewise, you are free to re-present work that has been
previously published elsewhere. We anticipate most papers and extended
abstracts to be presented a posters, with only a few selected for oral
presentation.

Please submit your paper using START:
https://www.softconf.com/acl2018/DLNLPLR/



ORGANIZERS


Gholamreza Haffari, Monash University

Colin Cherry, Google Research

George Foster, Google Research

Shahram Khadivi, eBay Research

Bahar Salehi, Melbourne University
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