This paper proposes the task of Visual COPA (VCOPA). Given a premise image and two alternative images, the task is to identify the more plausible alternative with their commonsense causal context. The VCOPA task is designed as its desirable machine system needs a more detailed understanding of the image, commonsense knowledge, and complex causal reasoning than state-of-the-art AI techniques. For that, we generate an evaluation dataset containing 380 VCOPA questions and over 1K images with various topics, which is amenable to automatic evaluation, and present the performance of baseline reasoning approaches as initial benchmarks for future systems.
|Title of host publication||LREC 2018 - 11th International Conference on Language Resources and Evaluation|
|Editors||Hitoshi Isahara, Bente Maegaard, Stelios Piperidis, Christopher Cieri, Thierry Declerck, Koiti Hasida, Helene Mazo, Khalid Choukri, Sara Goggi, Joseph Mariani, Asuncion Moreno, Nicoletta Calzolari, Jan Odijk, Takenobu Tokunaga|
|Publisher||European Language Resources Association (ELRA)|
|Number of pages||5|
|Publication status||Published - 2019|
|Event||11th International Conference on Language Resources and Evaluation, LREC 2018 - Miyazaki, Japan|
Duration: 2018 May 7 → 2018 May 12
|Name||LREC 2018 - 11th International Conference on Language Resources and Evaluation|
|Other||11th International Conference on Language Resources and Evaluation, LREC 2018|
|Period||18/5/7 → 18/5/12|
Bibliographical noteFunding Information:
This work was supported by Microsoft Research, and Institute for Information communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2017-0-01778,Development of Explainable Human-level Deep Machine Learning Inference Framework). S. Hwang is a corresponding author.
© LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.
All Science Journal Classification (ASJC) codes
- Linguistics and Language
- Library and Information Sciences
- Language and Linguistics