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1
Delving Deeper into Cross-lingual Visual Question Answering ...
Abstract: Visual question answering (VQA) is one of the crucial vision-and-language tasks. Yet, the bulk of research until recently has focused only on the English language due to the lack of appropriate evaluation resources. Previous work on cross-lingual VQA has reported poor zero-shot transfer performance of current multilingual multimodal Transformers and large gaps to monolingual performance, attributed mostly to misalignment of text embeddings between the source and target languages, without providing any additional deeper analyses. In this work, we delve deeper and address different aspects of cross-lingual VQA holistically, aiming to understand the impact of input data, fine-tuning and evaluation regimes, and interactions between the two modalities in cross-lingual setups. 1) We tackle low transfer performance via novel methods that substantially reduce the gap to monolingual English performance, yielding +10 accuracy points over existing transfer methods. 2) We study and dissect cross-lingual VQA across ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2202.07630
https://dx.doi.org/10.48550/arxiv.2202.07630
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2
xGQA: Cross-Lingual Visual Question Answering ...
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3
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ...
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4
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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5
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
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6
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ...
Pfeiffer, Jonas; Vulic, Ivan; Gurevych, Iryna. - : Apollo - University of Cambridge Repository, 2020
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7
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
Vulic, Ivan; Pfeiffer, Jonas; Ruder, Sebastian. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020
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8
AdapterHub: A Framework for Adapting Transformers
Pfeiffer, Jonas; Ruckle, Andreas; Poth, Clifton. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP 2020), 2020
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9
Common sense or world knowledge? Investigating adapter-based knowledge injection into pretrained transformers
Lauscher, Anne; Majewska, Olga; Ribeiro, Leonardo F. R.. - : Association for Computational Linguistics, 2020
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10
Explicit retrofitting of distributional word vectors
Glavaš, Goran; Vulić, Ivan. - : Association for Computational Linguistics, 2018
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