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1
Translation Error Detection as Rationale Extraction ...
Abstract: Recent Quality Estimation (QE) models based on multilingual pre-trained representations have achieved very competitive results when predicting the overall quality of translated sentences. Predicting translation errors, i.e. detecting specifically which words are incorrect, is a more challenging task, especially with limited amounts of training data. We hypothesize that, not unlike humans, successful QE models rely on translation errors to predict overall sentence quality. By exploring a set of feature attribution methods that assign relevance scores to the inputs to explain model predictions, we study the behaviour of state-of-the-art sentence-level QE models and show that explanations (i.e. rationales) extracted from these models can indeed be used to detect translation errors. We therefore (i) introduce a novel semi-supervised method for word-level QE and (ii) propose to use the QE task as a new benchmark for evaluating the plausibility of feature attribution, i.e. how interpretable model explanations are ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2108.12197
https://dx.doi.org/10.48550/arxiv.2108.12197
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2
Knowledge Distillation for Quality Estimation ...
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3
Knowledge Distillation for Quality Estimation ...
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4
Knowledge distillation for quality estimation
Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando. - : Association for Computational Linguistics, 2021
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5
Knowledge distillation for quality estimation
In: 5091 ; 5099 (2021)
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6
Unsupervised quality estimation for neural machine translation
In: 8 ; 539 ; 555 (2020)
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