# Building Markovian Generative Architectures over Pretrained LM Backbones for Efficient Task-Oriented Dialog Systems

arXiv: https://arxiv.org/abs/2204.06452 · HTML: https://mlsanity.com/paper/2204.06452/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://mlsanity.com/paper/1606.03777/) (2016)
- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2](https://mlsanity.com/paper/2012.03539/) (2020)
- [Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context](https://mlsanity.com/paper/1911.10484/) (2019)
- [Shades of BLEU, Flavours of Success: The Case of MultiWOZ](https://mlsanity.com/paper/2106.05555/) (2021)
- [A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning](https://mlsanity.com/paper/2009.08115/) (2020)
- [Paraphrase Augmented Task-Oriented Dialog Generation](https://mlsanity.com/paper/2004.07462/) (2020)
- [An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog](https://mlsanity.com/paper/1708.05956/) (2017)
- [Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems](https://mlsanity.com/paper/2109.04314/) (2021)
- [Flexibly-Structured Model for Task-Oriented Dialogues](https://mlsanity.com/paper/1908.02402/) (2019)
- [Tracking of enriched dialog states for flexible conversational information access](https://mlsanity.com/paper/1711.03381/) (2017)

## Built on by (most cited first, 4 of 4)

- [A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems](https://mlsanity.com/paper/2210.08692/) (2022)
- [An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought](https://mlsanity.com/paper/2407.15569/) (2024)
- [Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking](https://mlsanity.com/paper/2311.10271/) (2023)
- [The 2nd FutureDial Challenge: Dialog Systems with Retrieval Augmented Generation (FutureDial-RAG)](https://mlsanity.com/paper/2405.13084/) (2024)

## Abstract (arXiv)

Recently, Transformer based pretrained language models (PLMs), such as GPT2 and T5, have been leveraged to build generative task-oriented dialog (TOD) systems. A drawback of existing PLM-based models is their non-Markov architectures across turns, i.e., the whole history is used as the conditioning input at each turn. First, this brings inefficiencies in memory and computation. Furthermore, using the whole history increases model complexity and may hurt the training efficiency, especially when facing small amounts of labeled training data (the low-resource setting). In this paper, motivated by the observation that dialog states could be viewed as Markov states, we propose to build Markovian Generative Architectures (MGA) over PLM backbones for efficient TOD systems. Experiments on MultiWOZ2.1 show that in the rich-resource setting, the proposed Markov models reduce memory and time costs without performance degradation; in the low-resource setting, the training efficiency of the Markov models is more significant.