LINGUIST List 31.2110

Mon Jun 29 2020

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

Editor for this issue: Sarah Robinson <>

Date: 29-Jun-2020
From: Shlomo Argamon <>
Subject: Computational Linguistics, Text/Corpus Linguistics, Cognitive Science / Frontiers in Artificial Intelligence (Jrnl)
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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''


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

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

Page Updated: 29-Jun-2020