1 |
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Modelling Latent Translations for Cross-Lingual Transfer ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Visually Grounded Reasoning across Languages and Cultures ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Visually Grounded Reasoning across Languages and Cultures ...
|
|
|
|
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 {R}easoning over {V}ision and {L}anguage (MaRVL) by eliciting statements from ... : EMNLP 2021; Fangyu and Emanuele contributed equally; MaRVL website: https://marvl-challenge.github.io ...
|
|
Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences
|
|
URL: https://dx.doi.org/10.48550/arxiv.2109.13238 https://arxiv.org/abs/2109.13238
|
|
BASE
|
|
Hide details
|
|
12 |
Visually Grounded Reasoning across Languages and Cultures ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
CoQA: A Conversational Question Answering Challenge
|
|
|
|
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 249-266 (2019) (2019)
|
|
BASE
|
|
Show details
|
|
20 |
Universal Dependencies 2.2
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
|
|
BASE
|
|
Show details
|
|
|
|