# Task2Dial: A Novel Task and Dataset for Commonsense enhanced Task-based Dialogue Grounded in Documents

arXiv: https://arxiv.org/abs/2204.01061 · HTML: https://mlsanity.com/paper/2204.01061/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 3 Apr 2022.

## Builds on (most cited first, 19 of 19)

- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [The E2E Dataset: New Challenges For End-to-End Generation](https://mlsanity.com/paper/1706.09254/) (2017)
- [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://mlsanity.com/paper/2102.01672/) (2021)
- [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017)
- [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://mlsanity.com/paper/1909.05358/) (2019)
- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](https://mlsanity.com/paper/1810.06553/) (2018)
- [A Dataset for Document Grounded Conversations](https://mlsanity.com/paper/1809.07358/) (2018)
- [doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset](https://mlsanity.com/paper/2011.06623/) (2020)
- [From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots](https://mlsanity.com/paper/1801.01957/) (2018)
- [Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.00783/) (2021)
- [DoQA -- Accessing Domain-Specific FAQs via Conversational QA](https://mlsanity.com/paper/2005.01328/) (2020)
- [Incremental Transformer with Deliberation Decoder for Document Grounded Conversations](https://mlsanity.com/paper/1907.08854/) (2019)
- [Dimensions of Commonsense Knowledge](https://mlsanity.com/paper/2101.04640/) (2021)
- [Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning](https://mlsanity.com/paper/1802.00500/) (2018)
- [A Survey of Document Grounded Dialogue Systems (DGDS)](https://mlsanity.com/paper/2004.13818/) (2020)
- [Analysing Data-To-Text Generation Benchmarks](https://mlsanity.com/paper/1705.03802/) (2017)

## Built on by (most cited first, 7 of 7)

- [Plan-Grounded Large Language Models for Dual Goal Conversational Settings](https://mlsanity.com/paper/2402.01053/) (2024)
- [Improved Instruction Ordering in Recipe-Grounded Conversation](https://mlsanity.com/paper/2305.17280/) (2023)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features](https://mlsanity.com/paper/2309.11307/) (2023)
- [Leveraging Interesting Facts to Enhance User Engagement with Conversational Interfaces](https://mlsanity.com/paper/2404.06659/) (2024)
- [Grounded Complex Task Segmentation for Conversational Assistants](https://mlsanity.com/paper/2309.11271/) (2023)
- [Large-Scale User Behavior Analysis in Multimodal AI-Assisted Manual Task Execution](https://mlsanity.com/paper/2609.07594/) (2026)

## Abstract (arXiv)

This paper proposes a novel task on commonsense-enhanced task-based dialogue grounded in documents and describes the Task2Dial dataset, a novel dataset of document-grounded task-based dialogues, where an Information Giver (IG) provides instructions (by consulting a document) to an Information Follower (IF), so that the latter can successfully complete the task. In this unique setting, the IF can ask clarification questions which may not be grounded in the underlying document and require commonsense knowledge to be answered. The Task2Dial dataset poses new challenges: (1) its human reference texts show more lexical richness and variation than other document-grounded dialogue datasets; (2) generating from this set requires paraphrasing as instructional responses might have been modified from the underlying document; (3) requires commonsense knowledge, since questions might not necessarily be grounded in the document; (4) generating requires planning based on context, as task steps need to be provided in order. The Task2Dial dataset contains dialogues with an average 18.15 number of turns and 19.79 tokens per turn, as compared to 12.94 and 12 respectively in existing datasets. As such, learning from this dataset promises more natural, varied and less template-like system utterances.