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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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Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.127 Abstract: Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, as we show in this paper, these systems are also vulnerable to training attacks. Specifically, we propose a poisoning attack in which a malicious adversary inserts a small poisoned sample of monolingual text into the training set of a system trained using back-translation. This sample is designed to induce a specific, targeted translation behaviour, such as peddling misinformation. We present two methods for crafting poisoned examples, and show that only a tiny handful of instances, amounting to only 0.02% of the training set, is sufficient to enact a successful attack. We outline a defence method against said attacks, which partly ameliorates the problem. However, we stress that this is a blind-spot in modern NMT, demanding immediate attention. ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
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URL: https://dx.doi.org/10.48448/9mfk-s944 https://underline.io/lecture/26218-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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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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