LINGUIST List 29.780

Fri Feb 16 2018

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

Editor for this issue: Kenneth Steimel <kenlinguistlist.org>

Date: 12-Feb-2018
From: Aïda Elamrani-Raoult <aidaraoultfree.fr>
Subject: Learning Language in Humans and in Machines 2018 Conference
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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: < click here to access email >
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.lhmgmail.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


Page Updated: 16-Feb-2018