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Hits 1 – 5 of 5
1
Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bugliarello, Emanuele
;
Elliott, Desmond
. - : Underline Science Inc., 2021
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2
Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bugliarello, Emanuele
;
Elliott, Desmond
. - : Underline Science Inc., 2021
BASE
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3
Visually Grounded Reasoning across Languages and Cultures ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bugliarello, Emanuele
;
Collier, Nigel
. - : Underline Science Inc., 2021
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4
On Language Models for Creoles ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Miryam
;
Bugliarello, Emanuele
. - : Underline Science Inc., 2021
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5
Visually Grounded Reasoning across Languages and Cultures ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Nigel
;
Bugliarello, Emanuele
;
Elliott, Desmond
;
Liu, Fangyu
;
Maria Ponti, Edoardo
;
Reddy, Siva
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.818/ Abstract: The design of widespread vision-and-language datasets and pre-trained encoders directly adopts, or draws inspiration from, the concepts and images of ImageNet. While one can hardly overestimate how much this benchmark contributed to progress in computer vision, it is mostly derived from lexical databases and image queries in English, resulting in source material with a North American or Western European bias. Therefore, we devise a new protocol to construct an ImageNet-style hierarchy representative of more languages and cultures. In particular, we let the selection of both concepts and images be entirely driven by native speakers, rather than scraping them automatically. Specifically, we focus on a typologically diverse set of languages, namely, Indonesian, Mandarin Chinese, Swahili, Tamil, and Turkish. On top of the concepts and images obtained through this new protocol, we create a multilingual dataset for {M}ulticultur{a}l ...
Keyword:
Data Management System
;
Machine Learning
;
Machine translation
;
Natural Language Processing
URL:
https://underline.io/lecture/37516-visually-grounded-reasoning-across-languages-and-cultures
https://dx.doi.org/10.48448/645v-7718
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