RNN Approaches to Text Normalization: A Challenge · HackerPapers discussions

RNN Approaches to Text Normalization: A Challenge — mentioned by subho406

Tue, 12 Jun 2018 10:05:40 GMT

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…

Paper and discussion history. A mention is not an endorsement.

RNN Approaches to Text Normalization: A Challenge — mentioned by subho406

Tue, 12 Jun 2018 09:11:33 GMT

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.

Paper and discussion history. A mention is not an endorsement.

RNN Approaches to Text Normalization: A Challenge — mentioned by subho406

Tue, 12 Jun 2018 09:06:25 GMT

Hi, we tried using a single model for the entire seq-to-seq task but the number of examples in PLAIN is huge which causes the model to perform worse on other classes. The reason we used XGBoost was to separate the two very different tasks (predicting whether a word in normalized; predicting the sequence of normalized tokens). On the other hand, as mentioned when comparing text normalization systems it is more important to look at the exact kinds of errors made by the system (not only the overall accuracy). Our model showed improvement over the baseline model in…

Paper and discussion history. A mention is not an endorsement.