# Database Search Results Disambiguation for Task-Oriented Dialog Systems

arXiv: https://arxiv.org/abs/2112.08351 · HTML: https://mlsanity.com/paper/2112.08351/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 29 of 29)

- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [AmbigQA: Answering Ambiguous Open-domain Questions](https://mlsanity.com/paper/2004.10645/) (2020)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines](https://mlsanity.com/paper/2007.12720/) (2020)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://mlsanity.com/paper/2005.02877/) (2020)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context](https://mlsanity.com/paper/1911.10484/) (2019)
- [MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation](https://mlsanity.com/paper/2104.00773/) (2021)
- [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 and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions](https://mlsanity.com/paper/2109.05794/) (2021)
- [SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations](https://mlsanity.com/paper/2104.08667/) (2021)
- [MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation](https://mlsanity.com/paper/2010.05594/) (2020)
- [A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning](https://mlsanity.com/paper/2009.08115/) (2020)
- [Answer-based Adversarial Training for Generating Clarification Questions](https://mlsanity.com/paper/1904.02281/) (2019)
- [Situated and Interactive Multimodal Conversations](https://mlsanity.com/paper/2006.01460/) (2020)
- [ClarQ: A large-scale and diverse dataset for Clarification Question Generation](https://mlsanity.com/paper/2006.05986/) (2020)
- [Annotation Inconsistency and Entity Bias in MultiWOZ](https://mlsanity.com/paper/2105.14150/) (2021)
- [Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System](https://mlsanity.com/paper/2006.06814/) (2020)
- [MIMICS: A Large-Scale Data Collection for Search Clarification](https://mlsanity.com/paper/2006.10174/) (2020)
- [Multi-Domain Dialogue Acts and Response Co-Generation](https://mlsanity.com/paper/2004.12363/) (2020)
- [Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models](https://mlsanity.com/paper/1910.03756/) (2019)
- [Open-domain clarification question generation without question examples](https://mlsanity.com/paper/2110.09779/) (2021)
- [Interactive Classification by Asking Informative Questions](https://mlsanity.com/paper/1911.03598/) (2019)
- [Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions](https://mlsanity.com/paper/2008.07559/) (2020)
- [User Engagement Prediction for Clarification in Search](https://mlsanity.com/paper/2102.04163/) (2021)
- [Fuzzy Classification of Multi-intent Utterances](https://mlsanity.com/paper/2104.10830/) (2021)

## Built on by (most cited first, 10 of 10)

- [Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training](https://mlsanity.com/paper/2406.00222/) (2024)
- [AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries](https://mlsanity.com/paper/2406.19073/) (2024)
- [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://mlsanity.com/paper/2307.10172/) (2023)
- [Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction](https://mlsanity.com/paper/2410.18481/) (2024)
- [Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models](https://mlsanity.com/paper/2211.12503/) (2022)
- [Using Textual Interface to Align External Knowledge for End-to-End Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2305.13710/) (2023)
- [Revealing User Familiarity Bias in Task-Oriented Dialogue via Interactive Evaluation](https://mlsanity.com/paper/2305.13857/) (2023)
- [PicPersona-TOD : A Dataset for Personalizing Utterance Style in Task-Oriented Dialogue with Image Persona](https://mlsanity.com/paper/2504.17390/) (2025)
- [Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation](https://mlsanity.com/paper/2508.05535/) (2025)
- [CUEing User Simulators: Calibrated User Embeddings for Multi-Turn Benchmarking](https://mlsanity.com/paper/2610.02460/) (2026)

## Abstract (arXiv)

As task-oriented dialog systems are becoming increasingly popular in our lives, more realistic tasks have been proposed and explored. However, new practical challenges arise. For instance, current dialog systems cannot effectively handle multiple search results when querying a database, due to the lack of such scenarios in existing public datasets. In this paper, we propose Database Search Result (DSR) Disambiguation, a novel task that focuses on disambiguating database search results, which enhances user experience by allowing them to choose from multiple options instead of just one. To study this task, we augment the popular task-oriented dialog datasets (MultiWOZ and SGD) with turns that resolve ambiguities by (a) synthetically generating turns through a pre-defined grammar, and (b) collecting human paraphrases for a subset. We find that training on our augmented dialog data improves the model's ability to deal with ambiguous scenarios, without sacrificing performance on unmodified turns. Furthermore, pre-fine tuning and multi-task learning help our model to improve performance on DSR-disambiguation even in the absence of in-domain data, suggesting that it can be learned as a universal dialog skill. Our data and code will be made publicly available.