The Case for Perspective in Multimodal Datasets

Marcelo Viridiano, Tiago Timponi Torrent, Oliver Czulo, Arthur Lorenzi, Ely Matos, Frederico Belcavello


Abstract
This paper argues in favor of the adoption of annotation practices for multimodal datasets that recognize and represent the inherently perspectivized nature of multimodal communication. To support our claim, we present a set of annotation experiments in which FrameNet annotation is applied to the Multi30k and the Flickr 30k Entities datasets. We assess the cosine similarity between the semantic representations derived from the annotation of both pictures and captions for frames. Our findings indicate that: (i) frame semantic similarity between captions of the same picture produced in different languages is sensitive to whether the caption is a translation of another caption or not, and (ii) picture annotation for semantic frames is sensitive to whether the image is annotated in presence of a caption or not.
Anthology ID:
2022.nlperspectives-1.14
Volume:
Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
NLPerspectives
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
108–116
Language:
URL:
https://aclanthology.org/2022.nlperspectives-1.14
DOI:
Bibkey:
Cite (ACL):
Marcelo Viridiano, Tiago Timponi Torrent, Oliver Czulo, Arthur Lorenzi, Ely Matos, and Frederico Belcavello. 2022. The Case for Perspective in Multimodal Datasets. In Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022, pages 108–116, Marseille, France. European Language Resources Association.
Cite (Informal):
The Case for Perspective in Multimodal Datasets (Viridiano et al., NLPerspectives 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2022.nlperspectives-1.14.pdf
Data
Flickr30K EntitiesFlickr30k