The model specifications used for the Kaggle competition was a lot different than the one mentioned in the paper. The paper compares on the same test set used by https://arxiv.org/abs/1611.00068. DNC showed significant improvement over LSTM as a recurrent unit of a seq-to-seq model with almost zero unacceptable mistakes in certain semiotic classes. LSTM, on the other hand, is susceptible to these kinds of mistakes even when a lot of data is available.
I'm sorry for the misunderstanding. The reason we added the sentence because the model used in the competition was also based on DNC. But, changes were made when writing the paper, for instance, we did not use any attention mechanism at the seq-to-seq level in the competition. Besides, the paper concentrates more on comparing the kinds of errors made by the DNC network (avoiding unacceptable mistakes; not the overall accuracy), which shows an improvement over the LSTM model in the paper (https://arxiv.org/abs/1611.00068). On the other hand, overall accuracy was more important for the Kaggle competition.
We modified the sentence to say, "An earlier version of the approach used here has secured the 6th position in the Kaggle Russian Text Normalization Challenge by Google's Text Normalization Research Group".
The write up on that (from Google, who organized it and provided the data) was really interesting: http://blog.kaggle.com/2018/02/07/a-brief-summary-of-the-kag...