# The Eighth Dialog System Technology Challenge

arXiv: https://arxiv.org/abs/1911.06394 · HTML: https://mlsanity.com/paper/1911.06394/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 25 of 25)

- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [Zero-shot Generalization in Dialog State Tracking through Generative Question Answering](https://mlsanity.com/paper/2101.08333/) (2021)
- [On the Calibration and Uncertainty of Neural Learning to Rank Models](https://mlsanity.com/paper/2101.04356/) (2021)
- [Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021](https://mlsanity.com/paper/2205.02388/) (2022)
- [NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment](https://mlsanity.com/paper/2110.06536/) (2021)
- [Task-Oriented Dialogue System as Natural Language Generation](https://mlsanity.com/paper/2108.13679/) (2021)
- [Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2109.04066/) (2021)
- [Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues](https://mlsanity.com/paper/2103.00820/) (2021)
- [Video Dialog as Conversation about Objects Living in Space-Time](https://mlsanity.com/paper/2207.03656/) (2022)
- [DEUS: A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues](https://mlsanity.com/paper/2103.01287/) (2021)
- [Meta-evaluation of Conversational Search Evaluation Metrics](https://mlsanity.com/paper/2104.13453/) (2021)
- [Redwood: Using Collision Detection to Grow a Large-Scale Intent Classification Dataset](https://mlsanity.com/paper/2204.05483/) (2022)
- [FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking](https://mlsanity.com/paper/2407.13945/) (2024)
- [Multi-Modal Video Dialog State Tracking in the Wild](https://mlsanity.com/paper/2407.02218/) (2024)
- [Does Dialog Length matter for Next Response Selection task? An Empirical Study](https://mlsanity.com/paper/2101.09647/) (2021)
- [POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling](https://mlsanity.com/paper/2109.03039/) (2021)
- [EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification](https://mlsanity.com/paper/2204.13496/) (2022)
- [Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System](https://mlsanity.com/paper/2106.04835/) (2021)
- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)
- [Reducing Sensitivity on Speaker Names for Text Generation from Dialogues](https://mlsanity.com/paper/2305.13833/) (2023)
- [Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues](https://mlsanity.com/paper/2307.06703/) (2023)
- [Grounding is All You Need? Dual Temporal Grounding for Video Dialog](https://mlsanity.com/paper/2410.05767/) (2024)
- [Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT](https://mlsanity.com/paper/2105.13479/) (2021)
- [Disentangling Online Chats with DAG-Structured LSTMs](https://mlsanity.com/paper/2106.09024/) (2021)
- [Candidate Attended Dialogue State Tracking Using BERT](https://mlsanity.com/paper/2607.16021/) (2026)

## Abstract (arXiv)

This paper introduces the Eighth Dialog System Technology Challenge. In line with recent challenges, the eighth edition focuses on applying end-to-end dialog technologies in a pragmatic way for multi-domain task-completion, noetic response selection, audio visual scene-aware dialog, and schema-guided dialog state tracking tasks. This paper describes the task definition, provided datasets, and evaluation set-up for each track. We also summarize the results of the submitted systems to highlight the overall trends of the state-of-the-art technologies for the tasks.