Still not there? Comparing Traditional Sequence-to-Sequence Models to   Encoder-Decoder Neural Networks on Monotone String Translation Tasks
  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. 
Carsten Schnober and Steffen Eger and Erik-L\^an Do Dinh and Iryna   Gurevych
cs.CL