# SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching

arXiv: https://arxiv.org/abs/2110.11514 · HTML: https://mlsanity.com/paper/2110.11514/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 38 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)
- [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://mlsanity.com/paper/2004.06871/) (2020)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable](https://mlsanity.com/paper/1910.07931/) (2019)
- [A Knowledge-Grounded Neural Conversation Model](https://mlsanity.com/paper/1702.01932/) (2017)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [ConveRT: Efficient and Accurate Conversational Representations from Transformers](https://mlsanity.com/paper/1911.03688/) (2019)
- [Few-shot Natural Language Generation for Task-Oriented Dialog](https://mlsanity.com/paper/2002.12328/) (2020)
- [Building a Conversational Agent Overnight with Dialogue Self-Play](https://mlsanity.com/paper/1801.04871/) (2018)
- [Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding](https://mlsanity.com/paper/1807.01554/) (2018)
- [Dialog State Tracking: A Neural Reading Comprehension Approach](https://mlsanity.com/paper/1908.01946/) (2019)
- [End-to-End Task-Completion Neural Dialogue Systems](https://mlsanity.com/paper/1703.01008/) (2017)
- [Embedding Symbolic Knowledge into Deep Networks](https://mlsanity.com/paper/1909.01161/) (2019)
- [Overview of the Ninth Dialog System Technology Challenge: DSTC9](https://mlsanity.com/paper/2011.06486/) (2020)
- [Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/1704.03084/) (2017)
- [Paraphrase Augmented Task-Oriented Dialog Generation](https://mlsanity.com/paper/2004.07462/) (2020)
- [Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations](https://mlsanity.com/paper/2005.08866/) (2020)
- [RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems](https://mlsanity.com/paper/2012.14666/) (2020)
- [Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning](https://mlsanity.com/paper/1702.03274/) (2017)
- [HyST: A Hybrid Approach for Flexible and Accurate Dialogue State Tracking](https://mlsanity.com/paper/1907.00883/) (2019)
- [Conversation Learner -- A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems](https://mlsanity.com/paper/2004.04305/) (2020)
- [Robust Conversational AI with Grounded Text Generation](https://mlsanity.com/paper/2009.03457/) (2020)

## Built on by (most cited first, 3 of 3)

- [SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting](https://mlsanity.com/paper/2305.09067/) (2023)
- [SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking](https://mlsanity.com/paper/2402.02285/) (2024)
- [MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues](https://mlsanity.com/paper/2502.17268/) (2025)

## Abstract (arXiv)

In this paper we explore the use of symbolic knowledge and machine teaching to reduce human data labeling efforts in building neural task bots. We propose SYNERGY, a hybrid learning framework where a task bot is developed in two steps: (i) Symbolic knowledge to neural networks: Large amounts of simulated dialog sessions are generated based on task-specific symbolic knowledge which is represented as a task schema consisting of dialog flows and task-oriented databases. Then a pre-trained neural dialog model, SOLOIST, is fine-tuned on the simulated dialogs to build a bot for the task. (ii) Neural learning: The fine-tuned neural dialog model is continually refined with a handful of real task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the task bot. We validate SYNERGY on four dialog tasks. Experimental results show that SYNERGY maps task-specific knowledge into neural dialog models achieving greater diversity and coverage of dialog flows, and continually improves model performance with machine teaching, thus demonstrating strong synergistic effects of symbolic knowledge and machine teaching.