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Creator / Publisher:
Neubig, Graham (8)
The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (8)
Rijhwani, Shruti (4)
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Tsvetkov, Yulia (2)
., Hao (1)
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2021 (8)
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Hits 1 – 8 of 8
1
Explorations in Transfer Learning for OCR Post-Correction ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Neubig, Graham
;
Rijhwani, Shruti
. - : Underline Science Inc., 2021
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2
Efficient Test Time Adapter Ensembling for Low-resource Language Varieties ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Sebastian
;
Neubig, Graham
. - : Underline Science Inc., 2021
BASE
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3
Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Anastasopoulos, Antonios
;
Chaudhary, Aditi
. - : Underline Science Inc., 2021
BASE
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4
Distributionally Robust Multilingual Machine Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Ghazvininejad, Marjan
;
Levy, Daniel
. - : Underline Science Inc., 2021
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5
When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Anastasopoulos, Antonios
;
Chaudhary, Aditi
;
Neubig, Graham
;
Yin, Kayo
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.553/ Abstract: Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language. For example, the noun "wall'' has different lexical manifestations in Spanish --" pared’’ refers to an indoor wall while "muro’’ refers to an outside wall. However, this variety of lexical distinction may not be obvious to non-native learners unless the distinction is explained in such a way. In this work, we present a method for automatically identifying fine-grained lexical distinctions, and extracting concise descriptions explaining these distinctions in a human- and machine-readable format. We confirm the quality of these extracted descriptions in a language learning setup for two languages, Spanish and Greek, where we use them to teach non-native speakers when to translate a given ambiguous word into its different possible translations. ...
Keyword:
Computational Linguistics
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
URL:
https://dx.doi.org/10.48448/npdg-4076
https://underline.io/lecture/37743-when-is-wall-a-pared-and-when-a-muroquestion-extracting-rules-governing-lexical-selection
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6
Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Anastasopoulos, Antonios
;
Neubig, Graham
. - : Underline Science Inc., 2021
BASE
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7
Phrase-level Active Learning for Neural Machine Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Hu, Junjie
;
Neubig, Graham
. - : Underline Science Inc., 2021
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8
Dependency Induction Through the Lens of Visual Perception ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Hao
;
Bisk, Yonatan
. - : Underline Science Inc., 2021
BASE
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