E-commerce customers in developing nations like India tend to follow no fixed
format while entering shipping addresses. Parsing such addresses is challenging
because of a lack of inherent structure or hierarchy. It is imperative to
understand the language of addresses, so that shipments can be routed without
delays. In this paper, we propose a novel approach towards understanding
customer addresses by deriving motivation from recent advances in Natural
Language Processing (NLP). We also formulate different pre-processing steps for
addresses using a combination of edit distance and phonetic algorithms. Then we
approach the task of creating vector representations for addresses using
Word2Vec with TF-IDF, Bi-LSTM and BERT based approaches. We compare these
approaches with respect to sub-region classification task for North and South
Indian cities. Through experiments, we demonstrate the effectiveness of
generalized RoBERTa model, pre-trained over a large address corpus for language
modelling task. Our proposed RoBERTa model achieves a classification accuracy
of around 90% with minimal text preprocessing for sub-region classification
task outperforming all other approaches. Once pre-trained, the RoBERTa model
can be fine-tuned for various downstream tasks in supply chain like pincode
suggestion and geo-coding. The model generalizes well for such tasks even with
limited labelled data. To the best of our knowledge, this is the first of its
kind research proposing a novel approach of understanding customer addresses in
e-commerce domain by pre-training language models and fine-tuning them for
different purposes.

研究了如何使用自然语言处理和 RoBERTa 模型来理解发展中国家（例如印度）中电商客户的运输地址，该方法结合了诸如编辑距离和语音算法之类的预处理步骤，并最终使用 RoBERTa 模型进行分类。