# Bootstrapping Conversational Agents With Weak Supervision

## TL;DR

This paper introduces a framework called SLP that leverages weak supervision and data programming to efficiently bootstrap intent classifiers from chat logs, significantly reducing manual labeling effort in developing conversational agents.

## Contribution

The paper presents a novel search, label, and propagate framework that automates intent labeling from chat logs, reducing development time and effort for conversational agents.

## Key findings

- User study shows positive feedback for the approach.
- Data programming effectively auto-labels training data.
- Framework reduces labeling time from hours to minutes.

## Abstract

Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the development process. In many occasions agent developers have access to historical chat logs that can provide a good quantity as well as coverage of training examples. However, the cost of labeling them with tens to hundreds of intents often prohibits taking full advantage of these chat logs. In this paper, we present a framework called \textit{search, label, and propagate} (SLP) for bootstrapping intents from existing chat logs using weak supervision. The framework reduces hours to days of labeling effort down to minutes of work by using a search engine to find examples, then relies on a data programming approach to automatically expand the labels. We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling. While the system is developed for training conversational agents, the framework has broader application in significantly reducing labeling effort for training text classifiers.