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1
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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3
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications ...
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4
Improving Zero-Shot Translation by Disentangling Positional Information ...
Liu, Danni; Niehues, Jan; Cross, James. - : Association for Computational Linguistics, 2021
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5
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ...
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6
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ...
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7
Detecting Hallucinated Content in Conditional Neural Sequence Generation ...
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8
Alternative Input Signals Ease Transfer in Multilingual Machine Translation ...
Sun, Simeng; Fan, Angela; Cross, James. - : arXiv, 2021
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9
Improving Zero-Shot Translation by Disentangling Positional Information ...
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10
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment ...
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11
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ...
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12
Improving Zero-Shot Translation by Disentangling Positional Information
Liu, Danni; Li, Xian; Niehues, Jan. - : Association for Computational Linguistics, 2021
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13
Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover's Distance ...
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14
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ...
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15
Improving Zero-Shot Translation by Disentangling Positional Information ...
Liu, Danni; Niehues, Jan; Cross, James. - : arXiv, 2020
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16
Unsupervised quality estimation for neural machine translation
In: 8 ; 539 ; 555 (2020)
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17
An exploratory study on multilingual quality estimation
In: 366 ; 377 (2020)
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18
BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task
In: 1010 ; 1017 (2020)
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19
Findings of the WMT 2020 shared task on quality estimation
In: 743 ; 764 (2020)
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20
MLQE-PE: A multilingual quality estimation and post-editing dataset
Abstract: © 2020 The Authors. For reuse permissions, please contact the Authors. ; We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains eleven language pairs, with human labels for up to 10,000 translations per language pair in the following formats: sentence-level direct assessments and post-editing effort, and word-level good/bad labels. It also contains the post-edited sentences, as well as titles of the articles where the sentences were extracted from, and the neural MT models used to translate the text.
Keyword: cs.CL; machine translation
URL: http://hdl.handle.net/2436/624591
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