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Improving historical spelling normalization with bi-directional LSTMs and multi-task learning
Marcel Bollmann
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Anders Søgaard
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Paper Details:
Month: December
Year: 2016
Location: Osaka, Japan
Venue:
COLING |
Citations
URL
An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization
Gongbo Tang
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Fabienne Cap
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Eva Pettersson
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Joakim Nivre
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Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection
Erik-Lân Do Dinh
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Steffen Eger
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Iryna Gurevych
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Identifying beneficial task relations for multi-task learning in deep neural networks
Joachim Bingel
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Anders Søgaard
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Multi-Task Learning for Argumentation Mining in Low-Resource Settings
Claudia Schulz
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Steffen Eger
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Johannes Daxenberger
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Tobias Kahse
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Iryna Gurevych
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Evaluating Historical Text Normalization Systems: How Well Do They Generalize?
Alexander Robertson
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Sharon Goldwater
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Learning attention for historical text normalization by learning to pronounce
Marcel Bollmann
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Joachim Bingel
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Anders Søgaard
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Multi-Task Learning of Keyphrase Boundary Classification
Isabelle Augenstein
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Anders Søgaard
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Multitask Parsing Across Semantic Representations
Daniel Hershcovich
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Omri Abend
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Ari Rappoport
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Normalizing Medieval German Texts: from rules to deep learning
Natalia Korchagina
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Multi-task learning for historical text normalization: Size matters
Marcel Bollmann
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Anders Søgaard
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Joachim Bingel
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Encoder-Decoder Methods for Text Normalization
Massimo Lusetti
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Tatyana Ruzsics
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Anne Göhring
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Tanja Samardžić
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Elisabeth Stark
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Normalizing Early English Letters to Present-day English Spelling
Mika Hämäläinen
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Tanja Säily
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Jack Rueter
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Jörg Tiedemann
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Eetu Mäkelä
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PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection
Steffen Eger
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Andreas Rücklé
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Iryna Gurevych
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https://www.linguistics.rub.de/anselm/
https://github.com/comphist/norma
http://www.chokkan.org/software/crfsuite/
Field Of Study
Linguistic Trends
Embeddings
Task
Named Entity Recognition
Semantic Similarity
Machine Translation
Language
English
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