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While buying a product from the e-commerce websites, customers generally havea plethora of questions. From the perspective of both the e-commerce serviceprovider as well as the customers, there must be an effective questionanswering system to provide immediate answers to the user queries. Whilecertain questions can only be answered after using the product, there are manyquestions which can be answered from the product specification itself. Our worktakes a first step in this direction by finding out the relevant productspecifications, that can help answering the user questions. We propose anapproach to automatically create a training dataset for this problem. Weutilize recently proposed XLNet and BERT architectures for this problem andfind that they provide much better performance than the Siamese model,previously applied for this problem. Our model gives a good performance evenwhen trained on one vertical and tested across different verticals.

Using Large Pretrained Language Models for Answering User Queries from Product Specifications