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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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Ko, Wei-Jen; El-Kishky, Ahmed; Renduchintala, Adithya; Chaudhary, Vishrav; Goyal, Naman; Guzmán, Francisco; Fung, Pascale; Koehn, Philipp; Diab, Mona. - : arXiv, 2021
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Abstract:
The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures. In this work, we exploit this linguistic overlap to facilitate translating to and from a low-resource language with only monolingual data, in addition to any parallel data in the related high-resource language. Our method, NMT-Adapt, combines denoising autoencoding, back-translation and adversarial objectives to utilize monolingual data for low-resource adaptation. We experiment on 7 languages from three different language families and show that our technique significantly improves translation into low-resource language compared to other translation baselines. ... : ACL 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2105.15071 https://dx.doi.org/10.48550/arxiv.2105.15071
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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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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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