# Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems

arXiv: https://arxiv.org/abs/2011.08243 · HTML: https://mlsanity.com/paper/2011.08243/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

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

- [Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems](https://mlsanity.com/paper/2104.09088/) (2021)
- [Ontologically Faithful Generation of Non-Player Character Dialogues](https://mlsanity.com/paper/2212.10618/) (2022)
- [Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models](https://mlsanity.com/paper/2404.14772/) (2024)
- [Contextual Data Augmentation for Task-Oriented Dialog Systems](https://mlsanity.com/paper/2310.10380/) (2023)

## Abstract (arXiv)

Goal-oriented dialog systems enable users to complete specific goals like requesting information about a movie or booking a ticket. Typically the dialog system pipeline contains multiple ML models, including natural language understanding, state tracking and action prediction (policy learning). These models are trained through a combination of supervised or reinforcement learning methods and therefore require collection of labeled domain specific datasets. However, collecting annotated datasets with language and dialog-flow variations is expensive, time-consuming and scales poorly due to human involvement. In this paper, we propose an approach for automatically creating a large corpus of annotated dialogs from a few thoroughly annotated sample dialogs and the dialog schema. Our approach includes a novel goal-sampling technique for sampling plausible user goals and a dialog simulation technique that uses heuristic interplay between the user and the system (Alexa), where the user tries to achieve the sampled goal. We validate our approach by generating data and training three different downstream conversational ML models. We achieve 18 ? 50% relative accuracy improvements on a held-out test set compared to a baseline dialog generation approach that only samples natural language and entity value variations from existing catalogs but does not generate any novel dialog flow variations. We also qualitatively establish that the proposed approach is better than the baseline. Moreover, several different conversational experiences have been built using this method, which enables customers to have a wide variety of conversations with Alexa.