Character-level Convolutional Networks for Text Classification *
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several largescale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.
The paper presents a character-level convolutional network architecture and applies it to eight text classification problems on large datasets that the authors construct. It also presents comparative results from several word-based deep NN models as well as bag-of-ngrams models. The character-level ConvNets outperform word-based models on four out of eight datasets, when word-based data augmentation is used.The clarity and quality of writing are ok but the presentation of the method and results could have been much more clear. There are numerous grammatical and spelling errors.The originality is not very high since character-level models have been used for other tasks. The representation of the input as a fixed length vector of length 1014 characters is unintuitive, as one would like to have a story for handling documents of all lengths. The use of data augmentation using a thesaurus is not consistent with the stated advantage of character-level models: if words and even a thesaurus is available, then word-level tokenization can be performed. The experimental results contain a large number of comparison models as well as several variants of the proposed architecture: large vs small hidden layers, use of thesaurus augmentation, and lower-case characters versus both lower and upper-case ones.For four of the eight problems character-level models with data augmentation performed best and outperformed word-based models. In some cases character-level models without data augmentation outperformed word-level models as well. The data settings where character-level models performed best were also ones with user-generated content which might contain misspellings and ones with the largest number of training instances.Since the hyper-parameters of the different models were not tuned and there is no comparison to published state-of-the-art results (or models with state-of-the-art results), it is not clear what the significance of these experiments could be.  *****Thanks for the detailed response. It will be good to add the justification for dataset choice to the paper.