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NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures ...
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Improving Zero-shot Cross-lingual Transfer between Closely Related Languages by injecting Character-level Noise ...
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Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution ...
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Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation ...
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Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021 ...
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Revisiting Negation in Neural Machine Translation ...
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Abstract:
Read paper: NA Abstract: In this paper, we evaluate the translation of negation both automatically and manually, in English—German (EN—DE) and English—Chinese (EN—ZH). We show that the ability of neural machine translation (NMT) models to translate negation has improved with deeper and more advanced networks, although the performance varies between language pairs and translation directions. The accuracy of manual evaluation in EN—DE, DE—EN, EN—ZH, and ZH—EN is 95.7%, 94.8%, 93.4%, and 91.7%, respectively. In addition, we show that under-translation is the most significant error type in NMT, which contrasts with the more diverse error profile previously observed for statistical machine translation. To better understand the root of the under-translation of negation, we study the model's information flow and training data. While our information flow analysis does not reveal any deficiencies that could be used to detect or fix the under-translation of negation, we find that negation is often rephrased during ...
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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://underline.io/lecture/25803-revisiting-negation-in-neural-machine-translation https://dx.doi.org/10.48448/x27r-fa09
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Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation ...
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Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation ...
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Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models ...
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Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution ...
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Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT ...
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Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT ...
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Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias ...
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Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT ...
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Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
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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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