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We present an approach where two different models (Deep and Shallow) aretrained separately on the data and a weighted average of the outputs is takenas the final result. For the Deep approach, we use different combinations ofmodels like Convolution Neural Network, pretrained word2vec embeddings andLSTMs to get representations which are then used to train a Deep NeuralNetwork. For Clarity prediction, we also use an Attentive Pooling approach forthe pooling operation so as to be aware of the Title-Category pair. For theshallow approach, we use boosting technique LightGBM on features generatedusing title and categories. We find that an ensemble of these approaches does abetter job than using them alone suggesting that the results of the deep andshallow approach are highly complementary

CIKM AnalytiCup 2017 Lazada Product Title Quality Challenge An Ensemble of Deep and Shallow Learning to predict the Quality of Product Titles