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Pathologies of Pre-trained Language Models in Few-shot Fine-tuning ...
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MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning ...
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A Dataset and Baselines for Multilingual Reply Suggestion ...
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A Conditional Generative Matching Model for Multi-lingual Reply Suggestion ...
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XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation ...
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Say `YES' to Positivity: Detecting Toxic Language in Workplace Communications ...
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A Conditional Generative Matching Model for Multi-lingual Reply Suggestion ...
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Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer ...
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XtremeDistil: Multi-stage Distillation for Massive Multilingual Models ...
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Smart To-Do : Automatic Generation of To-Do Items from Emails ...
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Distilling BERT into Simple Neural Networks with Unlabeled Transfer Data ...
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Multi-Source Cross-Lingual Model Transfer: Learning What to Share ...
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Abstract:
Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP tasks. Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language by leveraging labeled data from other (source) languages. In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance. Unlike most existing methods that rely only on language-invariant features for CLTL, our approach coherently utilizes both language-invariant and language-specific features at instance level. Our model leverages adversarial networks to learn language-invariant features, and mixture-of-experts models to dynamically exploit the similarity between the target language and each individual source language. This enables our model to learn effectively what ... : ACL 2019 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.1810.03552 https://arxiv.org/abs/1810.03552
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Identifying Roles in Social Networks using Linguistic Analysis.
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