  Text Classification finds interesting applications in the pickup and delivery
services industry where customers require one or more items to be picked up
from a location and delivered to a certain destination. Classifying these
customer transactions into multiple categories helps understand the market
needs for different customer segments. Each transaction is accompanied by a
text description provided by the customer to describe the products being picked
up and delivered which can be used to classify the transaction. BERT-based
models have proven to perform well in Natural Language Understanding. However,
the product descriptions provided by the customers tend to be short, incoherent
and code-mixed (Hindi-English) text which demands fine-tuning of such models
with manually labelled data to achieve high accuracy. Collecting this labelled
data can prove to be expensive. In this paper, we explore Active Learning
strategies to label transaction descriptions cost effectively while using BERT
to train a transaction classification model. On TREC-6, AG's News Corpus and an
internal dataset, we benchmark the performance of BERT across different Active
Learning strategies in Multi-Class Text Classification.
