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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 ...
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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 ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.673/ Abstract: Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available. However, recent studies have also shown that the performance of MMT models is only marginally impacted when the associated image is replaced with an unrelated image or noise, which suggests that the visual context might not be exploited by the model at all. We hypothesize that this might be caused by the nature of the commonly used evaluation benchmark, also known as Multi30K, where the translations of image captions were prepared without actually showing the images to human translators. In this paper, we present a qualitative study that examines the role of datasets in stimulating the leverage of visual modality and we propose methods to highlight the importance of visual signals in the datasets which demonstrate improvements in reliance of models on the source ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing
URL: https://underline.io/lecture/37882-vision-matters-when-it-should-sanity-checking-multimodal-machine-translation-models
https://dx.doi.org/10.48448/4w38-v548
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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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