Dialog System Technology Challenge 7

Abstract

This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems. Recently, end-to-end dialog modeling approaches have been applied to various dialog tasks. The seventh DSTC (DSTC7) focuses on developing technologies related to end-to-end dialog systems for (1) sentence selection, (2) sentence generation and (3) audio visual scene aware dialog. This paper summarizes the overall setup and results of DSTC7, including detailed descriptions of the different tracks and provided datasets. We also describe overall trends in the submitted systems and the key results. Each track introduced new datasets and participants achieved impressive results using state-of-the-art end-to-end technologies.

This paper is presented at NIPS2018 2nd Conversational AI workshop

References in corpus (4)

• A Neural Conversational Model
• Enhanced LSTM for Natural Language Inference
• End-to-end Conversation Modeling Track in DSTC6
• Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7

Cited by in corpus (6)

• Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
• A Large-Scale Corpus for Conversation Disentanglement
• Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
• Is this Dialogue Coherent? Learning from Dialogue Acts and Entities
• Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization
• $C^3$: Compositional Counterfactual Contrastive Learning for Video-grounded Dialogues