31.2110, Calls: Computational Linguistics, Text/Corpus Linguistics, Cognitive Science / Frontiers in Artificial Intelligence (Jrnl)

[The LINGUIST List]

LINGUIST List: Vol-31-2110. Mon Jun 29 2020. ISSN: 1069 - 4875.

Subject: 31.2110, Calls:  Computational Linguistics, Text/Corpus Linguistics, Cognitive Science / Frontiers in Artificial Intelligence (Jrnl)

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Date: Mon, 29 Jun 2020 13:31:35
From: Shlomo Argamon [argamon at iit.edu]
Subject: Computational Linguistics, Text/Corpus Linguistics, Cognitive Science / Frontiers in Artificial Intelligence (Jrnl)

 
Full Title: Frontiers in Artificial Intelligence 


Linguistic Field(s): Cognitive Science; Computational Linguistics; Text/Corpus Linguistics 

Frontiers in AI: Language and Computation
Call for Commentary on ''Challenges and Prospects in Vision and Language
Research''

URL:
https://www.frontiersin.org/research-topics/14868/commentary-on-challenges-and
-prospects-in-vision-and-language-research

We invite open peer commentaries on the target article below, and on the
perspective it brings to the research area. Commentary is to be construed
broadly, to include (without being limited to):
- Substantive criticism directly responding to the target article,
- Different perspectives on the questions and issues raised by the target
article, and
- Alternative solutions to the problems raised by the target article.
The authors of the target article will have an opportunity to respond to the
commentaries as well.
The goal of these commentaries is to stimulate discussion in the research
community and so advance the state of thinking in the field. We solicit a
broad range of such responses, in line with this goal. The journal offers
multiple article types; typically, commentaries will be Perspective,
Conceptual Analysis, General Commentary, or Opinion type articles, but other
article types may be considered if relevant.

Target Article:
''Challenges and Prospects in Vision and Language Research'', by Kushal Kafle,
Robik Shrestha and Christopher Kanan

Abstract:
Language grounded image understanding tasks have often been proposed as a
method for evaluating progress in artificial intelligence. Ideally, these
tasks should test a plethora of capabilities that integrate computer vision,
reasoning, and natural language understanding.

However, the datasets and evaluation procedures used in these tasks are
replete with flaws which allows the vision and language (V&L) algorithms to
achieve a good performance without a robust understanding of vision and
language. We argue for this position based on several recent studies in V&L
literature and our own observations of dataset bias, robustness, and spurious
correlations. Finally, we propose that several of these challenges can be
mitigated by creation of carefully designed benchmarks.

Keywords: vision and language, deep learning, natural language processing,
computer vision, image classification, object detection, entity recognition,
sentiment analysis, question-answering, dialog systems, language and
computation




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