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

by   Arjun Bhardwaj, et al.

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


page 1

page 2

page 3

page 4


Few-shot Learning for Multi-label Intent Detection

In this paper, we study the few-shot multi-label classification for user...

Benchmarking Intent Detection for Task-Oriented Dialog Systems

Intent detection is a key component of modern goal-oriented dialog syste...

Modeling Intent, Dialog Policies and Response Adaptation for Goal-Oriented Interactions

Building a machine learning driven spoken dialog system for goal-oriente...

Example-Driven Intent Prediction with Observers

A key challenge of dialog systems research is to effectively and efficie...

Password Guessers Under a Microscope: An In-Depth Analysis to Inform Deployments

Password guessers are instrumental for assessing the strength of passwor...

Redwood: Using Collision Detection to Grow a Large-Scale Intent Classification Dataset

Dialog systems must be capable of incorporating new skills via updates o...

Zero-Shot Learning for Requirements Classification: An Exploratory Study

Context: Requirements engineering researchers have been experimenting wi...

Please sign up or login with your details

Forgot password? Click here to reset