  Intelligent Personal Assistants (IPAs) have become widely popular in recent
times. Most of the commercial IPAs today support a wide range of skills
including Alarms, Reminders, Weather Updates, Music, News, Factual
Questioning-Answering, etc. The list grows every day, making it difficult to
remember the command structures needed to execute various tasks. An IPA must
have the ability to communicate information about supported skills and direct
users towards the right commands needed to execute them. Users interact with
personal assistants in natural language. A query is defined to be a Help Query
if it seeks information about a personal assistant's capabilities, or asks for
instructions to execute a task. In this paper, we propose an interactive system
which identifies help queries and retrieves appropriate responses. Our system
comprises of a C-BiLSTM based classifier, which is a fusion of Convolutional
Neural Networks (CNN) and Bidirectional LSTM (BiLSTM) architectures, to detect
help queries and a semantic Approximate Nearest Neighbours (ANN) module to map
the query to an appropriate predefined response. Evaluation of our system on
real-world queries from a commercial IPA and a detailed comparison with popular
traditional machine learning and deep learning based models reveal that our
system outperforms other approaches and returns relevant responses for help
queries.
