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Deepreply - An Automatic Email Reply System with Unsupervised Cloze Translation and Deep Learning
Electronic mail (E-mail) has been the primary mode of communication for official purposes and it continues to be the same in all work environments even today. With the growing number of emails and most of them requiring only trivial replies, more tools are needed to generate replies to emails by reusing past replies. Although there are expert systems that can assist us in replying to incoming emails, they produce a generic reply to all. So an intelligent system that can generate replies for an incoming email in a very precise manner and generating the text reply in the user’s style is the identified requirement. This work is divided into two portions. First, translating an incoming email into cloze representation and extract the entities from it for generating a context, question and answer triplets. This is used for synthesising the training data for Extractive Question Answering later. The mentioned triplets are generated from a corpus of random emails belonging to different contexts and then the answers are extracted by recognising the named entities and random phrases of nouns from these paragraphs. The second ploy is to find the similarity between an incoming email that requires a reply and an old email that contains the reply to it. As a solution to these challenges, we propose a new deep neural network-based approach that relies on coarse-grained sentence modelling using CNN and a LSTM model. Our experimental results show that the approach outperforms the state-of-the-art approaches that are existing on a cleaner corpus.
Deep Learning, E-mail, Unsupervised, Questioning.
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