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Helpdesk is a key component of any large IT organization, where users can log a ticket about any issue they face related to IT infrastructure, administrative services, human resource services, etc. Normally, users have to assign appropriate set of labels to a ticket so that it could be routed to right domain expert who can help resolve the issue. In practice, the number of labels are very large and organized in form of a tree. It is non-trivial to describe the issue completely and attach appropriate labels unless one knows the cause of the problem and the related labels. Sometimes domain experts discuss the issue with the users and change the ticket labels accordingly, without modifying the ticket description. This results in inconsistent and badly labeled data, making it hard for supervised algorithms to learn from. In this paper, we propose a novel approach of creating a conversational helpdesk system, which will ask relevant questions to the user, for identification of the right category and will then raise a ticket on users' behalf. We use attention based seq2seq model to assign the hierarchical categories to tickets. We use a slot filling model to help us decide what questions to ask to the user, if the top-k model predictions are not consistent. We also present a novel approach to generate training data for the slot filling model automatically based on attention in the hierarchical classification model. We demonstrate via a simulated user that the proposed approach can give us a significant gain in accuracy on ticket-data without asking too many questions to users. Finally, we also show that our seq2seq model is as versatile as other approaches on publicly available datasets, as state of the art approaches.

In this paper we present the design, architecture and implementation of KNADIA, a conversational dialogue system for intra-enterprise use, providing knowledge-assisted question answering and transactional assistance to employees of a large organization. KNADIA has been deployed in production in TCS, a large organization with over 380,000 employees distributed globally; the system is currently supporting a few thousand active users making hundreds of queries per day. We identify, define and distinguish two distinct classes of use-cases: virtual assistance and knowledge synthesis, which we have found to cover a variety of enterprise needs. KNADIA supports both types of conversational agents, with multiple instances of each, while presenting a common digital persona. KNADIA disambiguates which agent should respond to each query. Further, since individual components use deep learning algorithms giving probabilistic outputs, the confidence with which different components answer is often required before answering or acting. The user's dialogue context also needs to be maintained judiciously. Due to these and many other challenges, the overall architecture of KNADIA is non-trivial. We present instances of HR-assistance and technical knowledge synthesis that are in production use in TCS along with accuracy figures and key performance metrics. Finally, we suggest that many elements of our architecture are also generally applicable to other complex deep learning systems.