  We propose a practical instant question answering (QA) system on product
pages of ecommerce services, where for each user query, relevant community
question answer (CQA) pairs are retrieved. User queries and CQA pairs differ
significantly in language characteristics making relevance learning difficult.
Our proposed transformer-based model learns a robust relevance function by
jointly learning unified syntactic and semantic representations without the
need for human labeled data. This is achieved by distantly supervising our
model by distilling from predictions of a syntactic matching system on user
queries and simultaneously training with CQA pairs. Training with CQA pairs
helps our model learning semantic QA relevance and distant supervision enables
learning of syntactic features as well as the nuances of user querying
language. Additionally, our model encodes queries and candidate responses
independently allowing offline candidate embedding generation thereby
minimizing the need for real-time transformer model execution. Consequently,
our framework is able to scale to large e-commerce QA traffic. Extensive
evaluation on user queries shows that our framework significantly outperforms
both syntactic and semantic baselines in offline as well as large scale online
A/B setups of a popular e-commerce service.
