# Pretraining Methods for Dialog Context Representation Learning

## TL;DR

This paper explores unsupervised pretraining objectives for dialog context encoders, proposing two novel methods that improve performance, convergence, data efficiency, and domain generalizability across dialog tasks.

## Contribution

It introduces two new pretraining objectives for dialog context learning and demonstrates their effectiveness over existing methods.

## Key findings

- Enhanced performance on dialog tasks
- Faster convergence and data efficiency
- Improved domain generalizability

## Abstract

This paper examines various unsupervised pretraining objectives for learning dialog context representations. Two novel methods of pretraining dialog context encoders are proposed, and a total of four methods are examined. Each pretraining objective is fine-tuned and evaluated on a set of downstream dialog tasks using the MultiWoz dataset and strong performance improvement is observed. Further evaluation shows that our pretraining objectives result in not only better performance, but also better convergence, models that are less data hungry and have better domain generalizability.