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being unpublished and later submission at another venue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