Dialog System Technology Challenge 7

By Koichiro Yoshino, Chiori Hori, Julien Perez, Luis Fernando D'Haro, Lazaros C. Polymenakos, R. Chulaka Gunasekara, Walter S. Lasecki, Jonathan K. Kummerfeld, Michel Galley, Chris Brockett and 7 more. 2019

38 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jianfeng Gao
2. Michel Galley
3. Minlie Huang
4. Chris Brockett
5. William B. Dolan
6. Ivan Vulic
7. Jason Aaron Edward Weston
8. Zhou Yu

In Computer Vision and Pattern Recognition

1. Jianfeng Gao
2. Michel Galley
3. Pengcheng He
4. Ivan Vulic
5. Goran Glavas
6. Chris Brockett
7. Zhou Yu
8. Yizhe Zhang

Citations in the corpus

• RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021, methodology): “Starting from the transformed representation of the last context token, we then simply fine-tune DialoGPT (transformer encoder plus the LM head) on the train portion of the DSTC-7 data set via causal language modeling, generating the correct response from the data set.”
• Jointly Optimizing Diversity and Relevance in Neural Response Generation (2019, background): “The field of neural response generation is advancing rapidly both in terms of research and commercial applications (Gao et al., 2019; Zhou et al., 2018; Yoshino et al., 2019; Zhang et al., 2019).”
• GODEL: Large-Scale Pre-Training for Goal-Directed Dialog (2022, background): “Our approach seeks to address a long-standing obstacle to general-purpose open-ended conversation models, namely a lack of robust automated evaluation criteria that can drive development (Gao et al., 2019).”