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The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation ...
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LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models ...
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Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications ...
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Improving Zero-Shot Translation by Disentangling Positional Information ...
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As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ...
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Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ...
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Detecting Hallucinated Content in Conditional Neural Sequence Generation ...
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Alternative Input Signals Ease Transfer in Multilingual Machine Translation ...
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Improving Zero-Shot Translation by Disentangling Positional Information ...
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XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment ...
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Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ...
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Improving Zero-Shot Translation by Disentangling Positional Information
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Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover's Distance ...
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MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ...
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Improving Zero-Shot Translation by Disentangling Positional Information ...
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Unsupervised quality estimation for neural machine translation
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In: 8 ; 539 ; 555 (2020)
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An exploratory study on multilingual quality estimation
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In: 366 ; 377 (2020)
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BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task
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In: 1010 ; 1017 (2020)
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Abstract:
© 2020 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://www.aclweb.org/anthology/2020.wmt-1.116/ ; This paper presents our submission to the WMT2020 Shared Task on Quality Estimation (QE). We participate in Task and Task 2 focusing on sentence-level prediction. We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems. In addition to training a feature-based regression model using glass-box quality indicators, we also test whether they can be used to predict MT quality directly with no supervision. We assess our systems in a multi-lingual setting and show that both types of approaches generalise well across languages. Our black-box QE models tied for the winning submission in four out of seven language pairs inTask 1, thus demonstrating very strong performance. The glass-box approaches also performed competitively, representing a light-weight alternative to the neural-based models.
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URL: http://hdl.handle.net/2436/623856
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Findings of the WMT 2020 shared task on quality estimation
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In: 743 ; 764 (2020)
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MLQE-PE: A multilingual quality estimation and post-editing dataset
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