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Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation ...
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BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling ...
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Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ...
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Learning Fast Adaptation on Cross-Accented Speech Recognition ...
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Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning ...
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Meta-Transfer Learning for Code-Switched Speech Recognition ...
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On the Importance of Word Order Information in Cross-lingual Sequence Labeling ...
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Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems ...
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Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables ...
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Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets ...
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Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences ...
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Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition ...
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Abstract:
In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching. Previous works addressing this challenge mainly focused on word-level aspects such as word embeddings. However, in many cases, languages share common subwords, especially for closely related languages, but also for languages that are seemingly irrelevant. Therefore, we propose Hierarchical Meta-Embeddings (HME) that learn to combine multiple monolingual word-level and subword-level embeddings to create language-agnostic lexical representations. On the task of Named Entity Recognition for English-Spanish code-switching data, our model achieves the state-of-the-art performance in the multilingual settings. We also show that, in cross-lingual settings, our model not only leverages closely related languages, but also learns from languages with different roots. Finally, we show that combining different subunits are crucial for capturing code-switching entities. ... : Accepted by EMNLP 2019 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1909.08504 https://dx.doi.org/10.48550/arxiv.1909.08504
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GlobalTrait: Personality Alignment of Multilingual Word Embeddings ...
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Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling ...
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Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems ...
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Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition ...
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Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning ...
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