# Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems

arXiv: https://arxiv.org/abs/2104.09088 · HTML: https://mlsanity.com/paper/2104.09088/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems](https://mlsanity.com/paper/1704.00057/) (2017)
- [Building a Conversational Agent Overnight with Dialogue Self-Play](https://mlsanity.com/paper/1801.04871/) (2018)
- [Dialog State Tracking: A Neural Reading Comprehension Approach](https://mlsanity.com/paper/1908.01946/) (2019)
- [User Modeling for Task Oriented Dialogues](https://mlsanity.com/paper/1811.04369/) (2018)
- [Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning](https://mlsanity.com/paper/1702.03274/) (2017)
- [End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1711.10712/) (2017)
- [Learning Robust Dialog Policies in Noisy Environments](https://mlsanity.com/paper/1712.04034/) (2017)
- [Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems](https://mlsanity.com/paper/2011.08243/) (2020)

## Built on by (most cited first, 14 of 14)

- [Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale](https://mlsanity.com/paper/2204.07309/) (2022)
- [Ontologically Faithful Generation of Non-Player Character Dialogues](https://mlsanity.com/paper/2212.10618/) (2022)
- [Teaching Models new APIs: Domain-Agnostic Simulators for Task Oriented Dialogue](https://mlsanity.com/paper/2110.06905/) (2021)
- [Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models](https://mlsanity.com/paper/2404.14772/) (2024)
- [Conversational Question Answering with Reformulations over Knowledge Graph](https://mlsanity.com/paper/2312.17269/) (2023)
- [Prompt-based Generative Approach towards Multi-Hierarchical Medical Dialogue State Tracking](https://mlsanity.com/paper/2203.09946/) (2022)
- [Prompt-tuning with Attribute Guidance for Low-resource Entity Matching](https://mlsanity.com/paper/2603.19321/) (2026)
- [A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example](https://mlsanity.com/paper/2205.06436/) (2022)
- [BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems](https://mlsanity.com/paper/2211.11982/) (2022)
- [Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level Generation](https://mlsanity.com/paper/2304.00884/) (2023)
- [Reinforcement Learning for Conversational Question Answering over Knowledge Graph](https://mlsanity.com/paper/2401.08460/) (2024)
- [Building Goal-Oriented Dialogue Systems with Situated Visual Context](https://mlsanity.com/paper/2111.11576/) (2021)
- [Manual-Guided Dialogue for Flexible Conversational Agents](https://mlsanity.com/paper/2208.07597/) (2022)
- [Flowstorm: Open-Source Platform with Hybrid Dialogue Architecture](https://mlsanity.com/paper/2212.09377/) (2022)

## Abstract (arXiv)

Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Training each component requires annotations which are hard to obtain for every new domain, limiting scalability of such systems. Similarly, rule-based dialogue systems require extensive writing and maintenance of rules and do not scale either. End-to-End dialogue systems, on the other hand, do not require module-specific annotations but need a large amount of data for training. To overcome these problems, in this demo, we present Alexa Conversations, a new approach for building goal-oriented dialogue systems that is scalable, extensible as well as data efficient. The components of this system are trained in a data-driven manner, but instead of collecting annotated conversations for training, we generate them using a novel dialogue simulator based on a few seed dialogues and specifications of APIs and entities provided by the developer. Our approach provides out-of-the-box support for natural conversational phenomena like entity sharing across turns or users changing their mind during conversation without requiring developers to provide any such dialogue flows. We exemplify our approach using a simple pizza ordering task and showcase its value in reducing the developer burden for creating a robust experience. Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over 50% improvement in turn-level action signature prediction accuracy.