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An Empirical Study of Factors Affecting Language-Independent Models
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An Empirical Study of Factors Affecting Language-Independent Models ...
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Abstract:
Scaling existing applications and solutions to multiple human languages has traditionally proven to be difficult, mainly due to the language-dependent nature of preprocessing and feature engineering techniques employed in traditional approaches. In this work, we empirically investigate the factors affecting language-independent models built with multilingual representations, including task type, language set and data resource. On two most representative NLP tasks -- sentence classification and sequence labeling, we show that language-independent models can be comparable to or even outperforms the models trained using monolingual data, and they are generally more effective on sentence classification. We experiment language-independent models with many different languages and show that they are more suitable for typologically similar languages. We also explore the effects of different data sizes when training and testing language-independent models, and demonstrate that they are not only suitable for ...
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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.1912.13106 https://arxiv.org/abs/1912.13106
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Raising Awareness of Conveyed Personality In Social Media Traces ...
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InsightMe: Raising Awareness of Conveyed Personality in Social Media Traces
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 10 No. 1 (2016): Tenth International AAAI Conference on Web and Social Media ; 2334-0770 ; 2162-3449 (2016)
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