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The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 (2)
., Harsh (1)
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Simple Search
Hits 1 – 9 of 9
1
IndoNLI: A Natural Language Inference Dataset for Indonesian ...
Mahendra, Rahmad
;
Aji, Alham Fikri
;
Louvan, Samuel
. - : arXiv, 2021
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2
IndoNLI: A Natural Language Inference Dataset for Indonesian ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Aji, Alham Fikri
;
Louvan, Samuel
. - : Underline Science Inc., 2021
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3
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
., Harsh
;
Bowman, Samuel R.
. - : Underline Science Inc., 2021
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4
Comparing Test Sets with Item Response Theory ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bowman, Samuel R.
;
Cho, Kyunghyun
. - : Underline Science Inc., 2021
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5
VisualSem: A High-quality Knowledge Graph for Vision and Language ...
Alberts, Houda
;
Huang, Teresa
;
Deshpande, Yash
;
Liu, Yibo
;
Cho, Kyunghyun
;
Vania, Clara
;
Calixto, Iacer
. - : arXiv, 2020
Abstract:
An exciting frontier in natural language understanding (NLU) and generation (NLG) calls for (vision-and-) language models that can efficiently access external structured knowledge repositories. However, many existing knowledge bases only cover limited domains, or suffer from noisy data, and most of all are typically hard to integrate into neural language pipelines. To fill this gap, we release VisualSem: a high-quality knowledge graph (KG) which includes nodes with multilingual glosses, multiple illustrative images, and visually relevant relations. We also release a neural multi-modal retrieval model that can use images or sentences as inputs and retrieves entities in the KG. This multi-modal retrieval model can be integrated into any (neural network) model pipeline. We encourage the research community to use VisualSem for data augmentation and/or as a source of grounding, among other possible uses. VisualSem as well as the multi-modal retrieval models are publicly available and can be downloaded in this ... : Accepted for publication at the 1st Multilingual Representation Learning workshop (MRL 2021) co-located with EMNLP 2021. 15 pages, 8 figures, 6 tables ...
Keyword:
Artificial Intelligence cs.AI
;
Computation and Language cs.CL
;
Computer Vision and Pattern Recognition cs.CV
;
E.0; E.2
;
FOS Computer and information sciences
URL:
https://dx.doi.org/10.48550/arxiv.2008.09150
https://arxiv.org/abs/2008.09150
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6
On understanding character-level models for representing morphology ...
Vania, Clara
. - : The University of Edinburgh, 2020
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7
On understanding character-level models for representing morphology
Vania, Clara
. - : The University of Edinburgh, 2020
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8
LINSPECTOR: Multilingual Probing Tasks for Word Representations
Şahin, Gözde Gül
;
Vania, Clara
;
Kuznetsov, Ilia
...
In: Computational Linguistics, Vol 46, Iss 2, Pp 335-385 (2020) (2020)
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9
CoNLL 2017 Shared Task System Outputs
Zeman, Daniel
;
Potthast, Martin
;
Straka, Milan
. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
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