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1
Language Models are Few-shot Multilingual Learners ...
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2
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling ...
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3
Are Multilingual Models Effective in Code-Switching? ...
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4
Zero-Shot Dialogue State Tracking via Cross-Task Transfer ...
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5
XPersona: Evaluating Multilingual Personalized Chatbot ...
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6
Learning Fast Adaptation on Cross-Accented Speech Recognition ...
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7
XPersona: Evaluating Multilingual Personalized Chatbot ...
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8
Meta-Transfer Learning for Code-Switched Speech Recognition ...
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9
On the Importance of Word Order Information in Cross-lingual Sequence Labeling ...
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10
Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems ...
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11
Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition ...
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 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1909.08504
https://dx.doi.org/10.48550/arxiv.1909.08504
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