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1
NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures ...
Vamvas, Jannis; Sennrich, Rico. - : arXiv, 2022
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2
Improving Zero-shot Cross-lingual Transfer between Closely Related Languages by injecting Character-level Noise ...
Aepli, Noëmi; Sennrich, Rico. - : arXiv, 2021
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3
On Biasing Transformer Attention Towards Monotonicity ...
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4
Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution ...
Emelin, Denis; Sennrich, Rico. - : Association for Computational Linguistics, 2021
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5
ELITR Multilingual Live Subtitling: Demo and Strategy ...
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6
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation ...
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7
Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021 ...
Zhang, Biao; Sennrich, Rico. - : ACL Anthology, 2021
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8
On Biasing Transformer Attention Towards Monotonicity ...
Rios, Annette; Amrhein, Chantal; Aepli, Noëmi. - : Association for Computational Linguistics, 2021
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9
Revisiting Negation in Neural Machine Translation ...
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10
Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.22 Abstract: Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in training data or domain shift. Recent work has tied these shortcomings to beam search -- the de facto standard inference algorithm in NMT -- and Eikema & Aziz (2020) propose to use Minimum Bayes Risk (MBR) decoding on unbiased samples instead. In this paper, we empirically investigate the properties of MBR decoding on a number of previously reported biases and failure cases of beam search. We find that MBR still exhibits a length and token frequency bias, owing to the MT metrics used as utility functions, but that MBR also increases robustness against copy noise in the training data and domain shift. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/fdyy-5b81
https://underline.io/lecture/25373-understanding-the-properties-of-minimum-bayes-risk-decoding-in-neural-machine-translation
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11
Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation ...
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12
Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models ...
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13
Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution ...
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14
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT ...
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15
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT ...
Voita, Elena; Sennrich, Rico; Titov, Ivan. - : ACL Anthology, 2021
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16
Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias ...
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17
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT ...
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18
On Biasing Transformer Attention Towards Monotonicity ...
NAACL 2021 2021; Aepli, Noëmi; Amrhein, Chantal. - : Underline Science Inc., 2021
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19
Universal rewriting via machine translation
Mallinson, Jonathan. - : The University of Edinburgh, 2021
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20
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
In: Zhang, Biao; Bapna, Ankur; Sennrich, Rico; Firat, Orhan (2021). Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation. In: International Conference on Learning Representations, Virtual, 3 May 2021 - 7 May 2021, ICLR. (2021)
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