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Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer ...
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Abstract:
Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfer tasks. Finding the most effective fine-tuning strategy to fine-tune these models on high-resource languages so that it transfers well to the zero-shot languages is a non-trivial task. In this paper, we propose a novel meta-optimizer to soft-select which layers of the pre-trained model to freeze during fine-tuning. We train the meta-optimizer by simulating the zero-shot transfer scenario. Results on cross-lingual natural language inference show that our approach improves over the simple fine-tuning baseline and X-MAML (Nooralahzadeh et al., 2020). ... : MetaNLP at ACL 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2107.09840 https://arxiv.org/abs/2107.09840
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Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer ...
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Improving Multilingual Neural Machine Translation with Auxiliary Source Languages ...
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How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation? ...
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How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation? ...
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EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ...
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A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification ...
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End-to-End Slot Alignment and Recognition for Cross-Lingual NLU ...
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EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints ...
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