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Multilingual Transfer Learning for C...

Winata, Genta Indra.

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Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling./
Author:	Winata, Genta Indra.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	121 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28697796
ISBN:	9798534657739

Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling.
Winata, Genta Indra.

Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 121 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: B.

Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.

This item must not be sold to any third party vendors.

Multilingualism is the ability of a speaker to communicate natively in more than one language. In multilingual communities, switching languages within a conversation, called code-switching, commonly occurs, and this creates a demand for multilingual dialogue and speech recognition systems to cater to this need. However, understanding code-switching utterances is a very challenging task for these systems because the model has to adapt to code-switching styles. Deep learning approaches have enabled natural language systems to achieve significant improvement towards human-level performance on languages with huge amounts of training data in recent years. However, they are unable to support numerous low-resource languages, mainly mixed languages. Also, code-switching, despite being a frequent phenomenon, is a characteristic only of spoken language and thus lacks transcriptions required for training deep learning models. On the other hand, conventional approaches to solving the low-resource issue in codeswitching are focused on applying linguistic theories to the statistical model. The constraints defined in these theories are useful. Still, they cannot be postulated as a universal rule for all code-switching scenarios, especially for languages that are syntactically divergent, such as English and Mandarin. In this thesis, we address the aforementioned issues by proposing language-agnostic multitask training methods. First, we introduce a meta-learning-based approach, meta-transfer learning, in which information is judiciously extracted from high-resource monolingual speech data to the code-switching domain. The meta-transfer learning quickly adapts the model to the codeswitching task from a number of monolingual tasks by learning to learn in a multi-task learning fashion. Second, we propose a novel multilingual meta-embeddings approach to effectively represent code-switching data by acquiring useful knowledge learned in other languages, learning the commonalities of closely related languages and leveraging lexical composition. The method is far more efficient compared to contextualized pre-trained multilingual models. Third, we introduce multi-task learning to integrate syntactic information as a transfer learning strategy to a language model and learn where to code-switch. To further alleviate the issue of data scarcity and limitations of linguistic theory, we propose a data augmentation method using Pointer-Gen, a neural network using a copy mechanism to teach the model the code-switch points from monolingual parallel sentences, and we use the augmented data for multilingual transfer learning. We disentangle the need for linguistic theory, and the model captures code-switching points by attending to input words and aligning the parallel words, without requiring any word alignments or constituency parsers. More importantly, the model can be effectively used for languages that are syntactically different, such as English and Mandarin, and it outperforms the linguistic theory-based models. In essence, we effectively tackle the data scarcity issue by introducing multilingual transfer learning methods to transfer knowledge from high-resource languages to the code-switching domain, and we compare their effectiveness with the conventional methods using linguistic theories.

ISBN: 9798534657739Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Code switching

Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling.
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500 $a Advisor: Fung, Pascale.
502 $a Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Multilingualism is the ability of a speaker to communicate natively in more than one language. In multilingual communities, switching languages within a conversation, called code-switching, commonly occurs, and this creates a demand for multilingual dialogue and speech recognition systems to cater to this need. However, understanding code-switching utterances is a very challenging task for these systems because the model has to adapt to code-switching styles. Deep learning approaches have enabled natural language systems to achieve significant improvement towards human-level performance on languages with huge amounts of training data in recent years. However, they are unable to support numerous low-resource languages, mainly mixed languages. Also, code-switching, despite being a frequent phenomenon, is a characteristic only of spoken language and thus lacks transcriptions required for training deep learning models. On the other hand, conventional approaches to solving the low-resource issue in codeswitching are focused on applying linguistic theories to the statistical model. The constraints defined in these theories are useful. Still, they cannot be postulated as a universal rule for all code-switching scenarios, especially for languages that are syntactically divergent, such as English and Mandarin. In this thesis, we address the aforementioned issues by proposing language-agnostic multitask training methods. First, we introduce a meta-learning-based approach, meta-transfer learning, in which information is judiciously extracted from high-resource monolingual speech data to the code-switching domain. The meta-transfer learning quickly adapts the model to the codeswitching task from a number of monolingual tasks by learning to learn in a multi-task learning fashion. Second, we propose a novel multilingual meta-embeddings approach to effectively represent code-switching data by acquiring useful knowledge learned in other languages, learning the commonalities of closely related languages and leveraging lexical composition. The method is far more efficient compared to contextualized pre-trained multilingual models. Third, we introduce multi-task learning to integrate syntactic information as a transfer learning strategy to a language model and learn where to code-switch. To further alleviate the issue of data scarcity and limitations of linguistic theory, we propose a data augmentation method using Pointer-Gen, a neural network using a copy mechanism to teach the model the code-switch points from monolingual parallel sentences, and we use the augmented data for multilingual transfer learning. We disentangle the need for linguistic theory, and the model captures code-switching points by attending to input words and aligning the parallel words, without requiring any word alignments or constituency parsers. More importantly, the model can be effectively used for languages that are syntactically different, such as English and Mandarin, and it outperforms the linguistic theory-based models. In essence, we effectively tackle the data scarcity issue by introducing multilingual transfer learning methods to transfer knowledge from high-resource languages to the code-switching domain, and we compare their effectiveness with the conventional methods using linguistic theories.
590 $a School code: 1223.
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650 4 $a Teaching methods. $3 595505
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