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Author
Schnober, Carsten; Eger, Steffen; Do Dinh, Erik-Lân; Gurevych, Iryna:

Title:
Still not there? Comparing traditional sequence-to-sequence models to encoder-decoder neural networks on monotone string translation tasks

Source:
In: The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING) Osaka : The COLING 2016 Organizing Committee (2016) , 1703-1714

URL of full text:
http://aclweb.org/anthology/C16-1160

Language:
Englisch

Document type
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Computerlinguistik, Computerunterstütztes Verfahren, Korrektur, Rechtschreibung, Text


Abstract(englisch):
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-Departments:
Information Center for Education

Notes: