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Autor*innen: Schnober, Carsten; Eger, Steffen; Do Dinh, Erik-Lân; Gurevych, Iryna
Titel: Still not there? Comparing traditional sequence-to-sequence models to encoder-decoder neural networks on monotone string translation tasks
Aus: The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka: The COLING 2016 Organizing Committee, 2016 , S. 1703-1714
URL: http://aclweb.org/anthology/C16-1160
Dokumenttyp: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Computerlinguistik; Computerunterstütztes Verfahren; Korrektur; Rechtschreibung; Text
Abstract (english): We analyze the performance of encoder-decoder neural models and compare them with well-known established methods. The latter represent different classes of traditional approaches that are applied to the monotone sequence-to-sequence tasks OCR post-correction, spelling correction, grapheme-to-phoneme conversion, and lemmatization. Such tasks are of practical relevance for various higher-level research fields including digital humanities, automatic text correction, and speech recognition. We investigate how well generic deep-learning approaches adapt to these tasks, and how they perform in comparison with established and more specialized methods, including our own adaptation of pruned CRFs. (DIPF/Orig.)
DIPF-Abteilung: Informationszentrum Bildung