29.780, Calls: Cog Sci, Comp Ling, Lang Acquisition, Psycholing/France

[The LINGUIST List]

LINGUIST List: Vol-29-780. Fri Feb 16 2018. ISSN: 1069 - 4875.

Subject: 29.780, Calls: Cog Sci, Comp Ling, Lang Acquisition, Psycholing/France

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Date: Fri, 16 Feb 2018 15:55:31
From: Aïda Elamrani-Raoult [aidaraoult at free.fr]
Subject: Learning Language in Humans and in Machines 2018 Conference

 
Full Title: Learning Language in Humans and in Machines 2018 Conference 
Short Title: L2HM 2018 

Date: 05-Jul-2018 - 06-Jul-2018
Location: Paris, France 
Contact Person: Conference Legrain
Meeting Email: legrain.lhm at gmail.com
Web Site: https://l2hm2018.sciencesconf.org/ 

Linguistic Field(s): Cognitive Science; Computational Linguistics; Language Acquisition; Psycholinguistics 

Call Deadline: 24-Mar-2018 

Meeting Description:

Recent advances in machine learning and big data open up the possibility of
reverse engineering cognitive language acquisition, with potential benefits to
both psycholinguistics and AI.

In this context, we are putting together an interdisciplinary 2-day workshop
aimed at bringing together researchers who work on language acquisition in
both fields. We are especially interested in:

1. Computational studies on language acquisition related questions, using 
realistic, large scale data (natural and/or artificial)

2. Psycholinguistic work on language acquisition that may have consequences
for computational models. For example, that implies a need for constraints/
assumptions in models


Call for Papers:

L2HM 2018: Learning Language in Humans and in Machines 2018 conference

Recent advances in machine learning and big data open up the possibility of
reverse engineering cognitive language acquisition, with potential benefits to
both psycholinguistics and AI.

In this context, we are putting together an interdisciplinary 2-day workshop
aimed at bringing together researchers who work on language acquisition in
both fields. We are especially interested in:

1. Computational studies on language acquisition related questions, using 
realistic, large scale data (natural and/or artificial).

2. Psycholinguistic work on language acquisition that may have consequences
for computational models. For example, that implies a need for constraints/
assumptions in models.

We invite submissions for poster presentations on experimental and/or modeling
approaches to human and machine language learning by 24 March 2018 (23.59
CET).
https://l2hm2018.sciencesconf.org/user/submissions

Submission Timeline:

February 9: abstract submission opens
March 24: abstract submission deadline 
May 1: notification of abstract acceptance
May 7: camera-ready abstract due 
June 1: poster uploaded to OSF 

Formatting guidelines:

Abstracts must be submitted as plain text.

Abstract text can be a maximum of  500 words (including references if used).

The body of the abstract should be fully anonymous and indicate the current
state of the work (i.e. complete or in progress--both will be considered).

Figures and tables may be appended to the submission in .PNG, .JPG, .TIFF, or
.PDF format (up to four files of max. 4 MB each).

Notification:

Each abstract will be rated by two reviewers for fit with the goals of L2HM,
completeness, methodological and analytical soundness,
originality/innovation/novelty, and clarity. Authors will be notified by May
1, 2018.
 
What research topics are relevant for abstract submission?

By its nature, this workshop will be interdisciplinary and methodologically
broad. We provide here some example topics for abstract submissions. This list
is far from exhaustive, so if you are unsure whether your topic is relevant
for the workshop, please don't hesitate to contact us at
legrain.lhm at gmail.com.

Examples of topics 

-One- and ''few''-shot learning
-Scalability and natural datasets for modelling human development 
-Incorporating cognitive constraints into word learning models
-How might infants and artificial agents use extra-linguistic cues to learn
new words and understand sentences?
-What are the differences and similarities in the learning of nouns, verbs and
function words?
-Semantic and syntactic bootstrapping: from developmental evidence to
modelling




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