# Zero-shot Generalization in Dialog State Tracking through Generative Question Answering

arXiv: https://arxiv.org/abs/2101.08333 · HTML: https://mlsanity.com/paper/2101.08333/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 51 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://mlsanity.com/paper/1904.00962/) (2019)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://mlsanity.com/paper/1806.08730/) (2018)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension](https://mlsanity.com/paper/1910.09753/) (2019)
- [Do NLP Models Know Numbers? Probing Numeracy in Embeddings](https://mlsanity.com/paper/1909.07940/) (2019)
- [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://mlsanity.com/paper/1606.03777/) (2016)
- [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://mlsanity.com/paper/2005.02877/) (2020)
- [Efficient Dialogue State Tracking by Selectively Overwriting Memory](https://mlsanity.com/paper/1911.03906/) (2019)
- [Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking](https://mlsanity.com/paper/1910.03544/) (2019)
- [SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching](https://mlsanity.com/paper/2005.05298/) (2020)
- [Building a Conversational Agent Overnight with Dialogue Self-Play](https://mlsanity.com/paper/1801.04871/) (2018)
- [Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2005.00891/) (2020)
- [End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems](https://mlsanity.com/paper/2010.06028/) (2020)

## Built on by (most cited first, 30 of 33)

- [Description-Driven Task-Oriented Dialog Modeling](https://mlsanity.com/paper/2201.08904/) (2022)
- [In-Context Learning for Few-Shot Dialogue State Tracking](https://mlsanity.com/paper/2203.08568/) (2022)
- [ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?](https://mlsanity.com/paper/2306.01386/) (2023)
- [SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems](https://mlsanity.com/paper/2110.06800/) (2021)
- ["Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking](https://mlsanity.com/paper/2207.14627/) (2022)
- [Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking](https://mlsanity.com/paper/2203.01552/) (2022)
- [OrchestraLLM: Efficient Orchestration of Language Models for Dialogue State Tracking](https://mlsanity.com/paper/2311.09758/) (2023)
- [QAConv: Question Answering on Informative Conversations](https://mlsanity.com/paper/2105.06912/) (2021)
- [NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation](https://mlsanity.com/paper/2105.14454/) (2021)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking](https://mlsanity.com/paper/2307.01453/) (2023)
- [DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning](https://mlsanity.com/paper/2212.02851/) (2022)
- [Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation](https://mlsanity.com/paper/2105.14445/) (2021)
- [A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking](https://mlsanity.com/paper/2201.05780/) (2022)
- [Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning](https://mlsanity.com/paper/2301.10915/) (2023)
- [Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries](https://mlsanity.com/paper/2402.13043/) (2024)
- [Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation](https://mlsanity.com/paper/2306.04724/) (2023)
- [Instilling Type Knowledge in Language Models via Multi-Task QA](https://mlsanity.com/paper/2204.13796/) (2022)
- [Continual Dialogue State Tracking via Example-Guided Question Answering](https://mlsanity.com/paper/2305.13721/) (2023)
- [Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema](https://mlsanity.com/paper/2303.16252/) (2023)
- [Inference is All You Need: Self Example Retriever for Cross-domain Dialogue State Tracking with ChatGPT](https://mlsanity.com/paper/2409.06243/) (2024)
- [XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking](https://mlsanity.com/paper/2204.05895/) (2022)
- [Controllable User Dialogue Act Augmentation for Dialogue State Tracking](https://mlsanity.com/paper/2207.12757/) (2022)
- [SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task](https://mlsanity.com/paper/2209.07742/) (2022)
- [A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding](https://mlsanity.com/paper/2409.15861/) (2024)
- [Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models](https://mlsanity.com/paper/2506.10504/) (2025)
- [Choice Fusion as Knowledge for Zero-Shot Dialogue State Tracking](https://mlsanity.com/paper/2302.13013/) (2023)
- [UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking](https://mlsanity.com/paper/2310.10492/) (2023)
- [Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents](https://mlsanity.com/paper/2405.17840/) (2024)
- [CorrectionLM: Self-Corrections with SLM for Dialogue State Tracking](https://mlsanity.com/paper/2410.18209/) (2024)

## Abstract (arXiv)

Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in task-oriented dialogs. In real-world settings with constantly changing services, DST systems must generalize to new domains and unseen slot types. Existing methods for DST do not generalize well to new slot names and many require known ontologies of slot types and values for inference. We introduce a novel ontology-free framework that supports natural language queries for unseen constraints and slots in multi-domain task-oriented dialogs. Our approach is based on generative question-answering using a conditional language model pre-trained on substantive English sentences. Our model improves joint goal accuracy in zero-shot domain adaptation settings by up to 9% (absolute) over the previous state-of-the-art on the MultiWOZ 2.1 dataset.