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In this report, we provide a comparative analysis of different techniques foruser intent classification towards the task of app recommendation. We analysethe performance of different models and architectures for multi-labelclassification over a dataset with a relative large number of classes and onlya handful examples of each class. We focus, in particular, on memory networkarchitectures, and compare how well the different versions perform under thetask constraints. Since the classifier is meant to serve as a module in apractical dialog system, it needs to be able to work with limited training dataand incorporate new data on the fly. We devise a 1-shot learning task to testthe models under the above constraint. We conclude that relatively simpleversions of memory networks perform better than other approaches. Although, fortasks with very limited data, simple non-parametric methods perform comparably,without needing the extra training data.

User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario