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1
XTREME-S: Evaluating Cross-lingual Speech Representations ...
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2
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation ...
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3
Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning ...
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4
mSLAM: Massively multilingual joint pre-training for speech and text ...
Bapna, Ankur; Cherry, Colin; Zhang, Yu. - : arXiv, 2022
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5
Examining Scaling and Transfer of Language Model Architectures for Machine Translation ...
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6
MAESTRO: Matched Speech Text Representations through Modality Matching ...
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7
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
In: https://hal.inria.fr/hal-03177623 ; 2021 (2021)
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Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets ...
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9
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents ...
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10
Joint Unsupervised and Supervised Training for Multilingual ASR ...
Bai, Junwen; Li, Bo; Zhang, Yu. - : arXiv, 2021
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11
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation ...
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12
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference ...
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13
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference ...
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14
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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15
Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation ...
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16
Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus ...
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17
Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation ...
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18
Investigating Multilingual NMT Representations at Scale ...
Abstract: Multilingual Neural Machine Translation (NMT) models have yielded large empirical success in transfer learning settings. However, these black-box representations are poorly understood, and their mode of transfer remains elusive. In this work, we attempt to understand massively multilingual NMT representations (with 103 languages) using Singular Value Canonical Correlation Analysis (SVCCA), a representation similarity framework that allows us to compare representations across different languages, layers and models. Our analysis validates several empirical results and long-standing intuitions, and unveils new observations regarding how representations evolve in a multilingual translation model. We draw three major conclusions from our analysis, with implications on cross-lingual transfer learning: (i) Encoder representations of different languages cluster based on linguistic similarity, (ii) Representations of a source language learned by the encoder are dependent on the target language, and vice-versa, and ... : Paper at EMNLP 2019 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://dx.doi.org/10.48550/arxiv.1909.02197
https://arxiv.org/abs/1909.02197
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19
Simple, Scalable Adaptation for Neural Machine Translation ...
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20
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges ...
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