28.2179, Calls: Comp Ling/France

[The LINGUIST List]

LINGUIST List: Vol-28-2179. Tue May 09 2017. ISSN: 1069 - 4875.

Subject: 28.2179, Calls: Comp Ling/France

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Date: Tue, 09 May 2017 14:03:12
From: Stergios Chatzikyriakidis [stergios.chatzikyriakidis at gu.se]
Subject: Workshop on Computing Natural Language Inference

 
Full Title: Workshop on Computing Natural Language Inference 
Short Title: CONLI 

Date: 19-Sep-2017 - 19-Sep-2017
Location: Montpellier, France 
Contact Person: Stergios Chatzikyriakidis
Meeting Email: stergios.chatzikyriakidis at gu.se
Web Site: https://sites.google.com/view/conli/home 

Linguistic Field(s): Computational Linguistics 

Call Deadline: 10-Jul-2017 

Meeting Description:

Central within a theory of formal semantics for Natural Language (NL) is the
study of Natural Language Inference (NLI). Roughly put, NLI is the task of
determining whether an NL hypothesis can be inferred from an NL premise. Human
beings do not only have the ability to understand infinitely many NL sentences
but can also reason about these. In effect, understanding a NL sentence
amounts (among other things) to knowing what can and cannot be inferred from
such a sentence. It is thus no surprise that NLI is a central field of
computational semantics. As Cooper et al. (1996) aptly put it, `inferential
ability is not only a central manifestation of semantic competence but is in
fact centrally constitutive of it'. Inferential ability, according to Cooper
et al., is the best way to test the semantic adequacy of NLP systems. However,
the three main datasets that have been designed to be used as test suites for
such NLP systems are inadequate. The FraCas test suite (Cooper et al. 1996)
offers good coverage in terms of inference types at the cost of unnatural data
(data are constructed) and small size. The RTE platforms (Dagan et al. 2005)
offer naturally occurring data at the cost of semantic coverage, while the
latest SNLI platform (Bowman et al 2015), offers a huge crowdsourced platform
but does not capture generic inferences, given that it is always dependent on
the specific situation described by the caption. In general, it seems that
existing platforms are limited to certain aspects of inference, and fail to
represent the full complexity of NLI. Furthermore, dialogue -- arguably one of
the most central aspects of human communication -- is left outside in all
three cases. Finally, with a creation of large image and language corpora
textual inference may be related to visual representations that the text is
referring to: both in terms of identifying a complement to textual
underspecification and in identification of perceptual features as types of
situations licensing certain kinds of inferences.


Call for Papers:

This workshop aims to create a forum where the issue of constructing NLI
platforms will be discussed from both a theoretical and implementational point
of view. We are further aiming at bringing together people from different
computational semantics backgrounds, but who, at the same time, share the goal
of creating a platform that is useful regardless the approach one takes w.r.t
NLI.  We invite 2-page  abstract submissions (references included) on
theoretical and implementational aspects of NLI. We specifically encourage
submissions on the construction and development of state of the art platforms
for NLI. The link to the submission site as well as formatting instructions
can be found here. Deadline for submission is the 10th of July 2017.

Topics of interest:

- Natural Language Inference broadly construed
- Theoretical and Implementational Issues in NLI
- Methodological Issues in Constructing state-of-the-art NLI platforms
- NLI in Dialogue Settings
- Non-logical Inference and Reasoning Under Implicit Assumptions

Organizing Committee:

Stergios Chatzikyriakidis, University of Gothenburg
Robin Cooper, University of Gothenburg
Simon Dobnik, University of Gothenburg
Staffan Larsson, University of Gothenburg

Program Committee:

Daisuke Bekki, Ochanomizu University
Jean-Philippe Bernandy, University of Gothenburg
Ellen Breitholtz, University of Gothenburg
Cleo Condoravdi, Stanford University
Ido Dagan, Bar Ilan University
Katrin Erk, University of Texas at Austin
Christine Howes, University of Gothenburg
Ai Kawazoe, Ochanomizu University
Reinhard Muskens, Tilburg University
 Mathieu Lafourcade, University of Montpellier
Zhaohui Luo, Royal Holloway, University of London
Koji Mineshima, Ochanomizu University
 Larry Moss, University of Indiana
Christopher Potts, Stanford University
Annie Zaenen, Stanford University
Lasha Abzianidze, University of Groningen




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