# Conversational Help for Task Completion and Feature Discovery in Personal Assistants

## TL;DR

This paper presents an interactive system for personal assistants that detects help queries and provides relevant responses, improving user understanding of available skills and commands through a neural network-based classifier and semantic matching.

## Contribution

It introduces a novel C-BiLSTM classifier combined with semantic ANN for effective help query detection and response retrieval in IPAs, outperforming existing models.

## Key findings

- System outperforms traditional models in help query detection.
- Accurately retrieves relevant responses for user help queries.
- Effective in real-world commercial IPA scenarios.

## Abstract

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.