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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Self-Supervised Curriculum Learning for Spelling Error Correction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gan, Zifa; Xu, Hongfei. - : Underline Science Inc., 2021 BASE Show details 2 Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Liu, Qiuhui; van Genabith, Josef. - : Underline Science Inc., 2021 BASE Show details 3 Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Liu, Qiuhui; van Genabith, Josef; Xiong, Deyi; Xu, Hongfei. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.46 Abstract: Neural machine translation has achieved great success in bilingual settings, as well as in multilingual settings. With the increase of the number of languages, multilingual systems tend to underperform their bilingual counterparts. Model capacity has been found crucial for massively multilingual NMT to support language pairs with varying typological characteristics. Previous work increases the modeling capacity by deepening or widening the Transformer. However, modeling cardinality based on aggregating a set of transformations with the same topology has been proven more effective than going deeper or wider when increasing capacity. In this paper, we propose to efficiently increase the capacity for multilingual NMT by increasing the cardinality. Unlike previous work which feeds the same input to several transformations and merges their outputs into one, we present a Multi-Input-Multi-Output (MIMO) architecture that allows each transformation ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/29pk-ag57 https://underline.io/lecture/25472-modeling-task-aware-mimo-cardinality-for-efficient-multilingual-neural-machine-translation BASE Hide details 4 Transformer-based NMT : modeling, training and implementation Xu, Hongfei. - : Saarländische Universitäts- und Landesbibliothek, 2021 BASE Show details 5 Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers ... Xu, Hongfei; van Genabith, Josef; Liu, Qiuhui. - : arXiv, 2020 BASE Show details

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