Semi-supervised Interactive Intent Labeling

TL;DR

This paper presents an interactive intent labeling system for dialogue systems that leverages advanced clustering, data augmentation, and visualization techniques to improve labeling efficiency and accuracy.

Contribution

It introduces an enhanced clustering-based labeling approach with BERT, seed data selection, data balancing, and augmentation, significantly reducing labeling effort.

Findings

Achieved over 10% improvement in clustering accuracy.

Demonstrated reduced time and effort in labeling datasets.

Extended deep clustering with BERT and paraphrasing techniques.

Abstract

Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS. In this work, we showcase an Intent Bulk Labeling system where SDS developers can interactively label and augment training data from unlabeled utterance corpora using advanced clustering and visual labeling methods. We extend the Deep Aligned Clustering work with a better backbone BERT model, explore techniques to select the seed data for labeling, and develop a data balancing method using an oversampling technique that utilizes paraphrasing models. We also look at the effect of data augmentation on the clustering process. Our results show that we can achieve over 10% gain in clustering accuracy on some datasets using the combination of the above techniques. Finally, we extract utterance embeddings from the clustering model and plot the data to interactively bulk label the samples, reducing the time and effort for data labeling of the whole dataset significantly.