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., Iryna (6)
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 (6)
., Sebastian (1)
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Hits 1 – 6 of 6
1
The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes ...
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
;
., Iryna
;
Reimers, Nils
. - : Underline Science Inc., 2021
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2
Green NLP panel ...
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
;
., Iryna
;
Arase, Yuki
. - : Underline Science Inc., 2021
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3
Investigating label suggestions for opinion mining in German Covid-19 social media ...
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
;
., Iryna
;
Beck, Tilman
. - : Underline Science Inc., 2021
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4
Coreference Reasoning in Machine Reading Comprehension ...
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
;
., Iryna
;
Moosavi, Nafise Sadat
. - : Underline Science Inc., 2021
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5
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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
;
., Iryna
;
., Sebastian
;
Pfeiffer, Jonas
;
Rust, Phillip
;
Vulić, Ivan
. - : Underline Science Inc., 2021
Abstract:
Read paper: https://www.aclanthology.org/2021.acl-long.243 Abstract: In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance. We study a set of nine typologically diverse languages with readily available pretrained monolingual models on a set of five diverse monolingual downstream tasks. We first aim to establish, via fair and controlled comparisons, if a gap between the multilingual and the corresponding monolingual representation of that language exists, and subsequently investigate the reason for any performance difference. To disentangle conflating factors, we train new monolingual models on the same data, with monolingually and multilingually trained tokenizers. We find that while the pretraining data size is an important factor, a designated monolingual tokenizer plays an equally important role in the downstream performance. Our results show that ...
Keyword:
Computational Linguistics
;
Condensed Matter Physics
;
Deep Learning
;
Electromagnetism
;
FOS Physical sciences
;
Information and Knowledge Engineering
;
Neural Network
;
Semantics
URL:
https://underline.io/lecture/25575-how-good-is-your-tokenizerquestion-on-the-monolingual-performance-of-multilingual-language-models
https://dx.doi.org/10.48448/bdjq-kw21
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6
Metaphor Generation with Conceptual Mappings ...
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
;
., Iryna
;
Chakrabarty, Tuhin
. - : Underline Science Inc., 2021
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