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Parsing with Pretrained Language Models, Multiple Datasets, and Dataset Embeddings ...
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On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions ...
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Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
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Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
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DaN+: Danish Nested Named Entities and Lexical Normalization ...
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From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ...
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From Masked-Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ...
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Lexical Normalization for Code-switched Data and its Effect on POS-tagging ...
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Abstract:
Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of manynatural language processing tasks on social media. Yet, using multiple languages in one utterance, also called code-switching (CS), is frequently overlooked by these normalization systems, despite its common use in social media. In this paper, we propose three normalization models specifically designed to handle code-switched data which we evaluate for two language pairs: Indonesian-English (Id-En) and Turkish-German (Tr-De). For the latter, we introduce novel normalization layers and their corresponding language ID and POS tags for the dataset, and evaluate the downstream effect of normalization on POS tagging. Results show that our CS-tailored normalization models outperform Id-En state of the art and Tr-De monolingual models, and lead to 5.4% relative performance increase for POS tagging as compared to unnormalized input. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2006.01175 https://arxiv.org/abs/2006.01175
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Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor
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In: Computational Linguistics, Vol 46, Iss 2, Pp 487-497 (2020) (2020)
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Bleaching Text: Abstract Features for Cross-lingual Gender Prediction ...
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