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1
Pushing the right buttons: adversarial evaluation of quality estimation
In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022)
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2
Reverse racism: the construction of a slip narrative ; Racismo inverso: la construcción de una narrativa deslizante ; Racismo reverso: a construção de uma narrativa de esquiva
In: Signótica; Vol. 34 (2022) ; Signótica; v. 34 (2022) ; 2316-3690 ; 0103-7250 (2022)
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3
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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5
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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6
When Does Translation Require Context? A Data-driven, Multilingual Exploration ...
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7
Measuring and Increasing Context Usage in Context-Aware Machine Translation ...
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8
SPECTRA: Sparse Structured Text Rationalization ...
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9
Do Context-Aware Translation Models Pay the Right Attention? ...
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10
Findings of the WMT 2021 Shared Task on Quality Estimation ...
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11
Do Context-Aware Translation Models Pay the Right Attention? ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.65 Abstract: Context-aware machine translation models are designed to leverage contextual information, but often fail to do so. As a result, they inaccurately disambiguate pronouns and polysemous words that require context for resolution. In this paper, we ask several questions: What contexts do human translators use to resolve ambiguous words? Are models paying large amounts of attention to the same context? What if we explicitly train them to do so? To answer these questions, we introduce SCAT (Supporting Context for Ambiguous Translations), a new English-French dataset comprising supporting context words for 14K translations that professional translators found useful for pronoun disambiguation. Using SCAT, we perform an in-depth analysis of the context used to disambiguate, examining positional and lexical characteristics of the supporting words. Furthermore, we measure the degree of alignment between the model's attention scores and the supporting ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25426-do-context-aware-translation-models-pay-the-right-attentionquestion
https://dx.doi.org/10.48448/20nd-s715
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12
Findings of the WMT 2021 shared task on quality estimation
In: 689 ; 730 (2021)
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13
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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14
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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15
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ...
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16
Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning ...
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17
Findings of the WMT 2020 shared task on quality estimation
In: 743 ; 764 (2020)
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18
MLQE-PE: A multilingual quality estimation and post-editing dataset
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19
Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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20
Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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