# AUTODIAL: Efficient Asynchronous Task-Oriented Dialogue Model

arXiv: https://arxiv.org/abs/2303.06245 · HTML: https://mlsanity.com/paper/2303.06245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [Multi-Task Learning as a Bargaining Game](https://mlsanity.com/paper/2202.01017/) (2022)
- [Muppet: Massive Multi-task Representations with Pre-Finetuning](https://mlsanity.com/paper/2101.11038/) (2021)
- [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://mlsanity.com/paper/1909.05358/) (2019)
- [ParlAI: A Dialog Research Software Platform](https://mlsanity.com/paper/1705.06476/) (2017)
- [Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems](https://mlsanity.com/paper/1807.11125/) (2018)
- [Annotation Inconsistency and Entity Bias in MultiWOZ](https://mlsanity.com/paper/2105.14150/) (2021)

## Abstract (arXiv)

As large dialogue models become commonplace in practice, the problems surrounding high compute requirements for training, inference and larger memory footprint still persists. In this work, we present AUTODIAL, a multi-task dialogue model that addresses the challenges of deploying dialogue model. AUTODIAL utilizes parallel decoders to perform tasks such as dialogue act prediction, domain prediction, intent prediction, and dialogue state tracking. Using classification decoders over generative decoders allows AUTODIAL to significantly reduce memory footprint and achieve faster inference times compared to existing generative approach namely SimpleTOD. We demonstrate that AUTODIAL provides 3-6x speedups during inference while having 11x fewer parameters on three dialogue tasks compared to SimpleTOD. Our results show that extending current dialogue models to have parallel decoders can be a viable alternative for deploying them in resource-constrained environments.