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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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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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On Biasing Transformer Attention Towards Monotonicity ...
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Abstract:
Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.354/ Abstract: Many sequence-to-sequence tasks in natural language processing are roughly monotonic in the alignment between source and target sequence, and previous work has facilitated or enforced learning of monotonic attention behavior via specialized attention functions or pretraining. In this work, we introduce a monotonicity loss function that is compatible with standard attention mechanisms and test it on several sequence-to-sequence tasks: grapheme-to-phoneme conversion, morphological inflection, transliteration, and dialect normalization. Experiments show that we can achieve largely monotonic behavior. Performance is mixed, with larger gains on top of RNN baselines. General monotonicity does not benefit transformer multihead attention, however, we see isolated improvements when only a subset of heads is biased towards monotonic behavior. ...
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Keyword:
Artificial Intelligence; Computer Science and Engineering; Intelligent System; Natural Language Processing; Psycholinguistics
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URL: https://underline.io/lecture/19654-on-biasing-transformer-attention-towards-monotonicity https://dx.doi.org/10.48448/x8nz-nn78
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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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