Demo Data from EDBT 2026 / reviewers in the wild / expert
Mahmoud Adada
dblp:224/1951
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0002-3713-8607ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Question answering and dialogue systems · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Question answering and dialogue systems › task-oriented dialogue
dialogue state tracking |
0.5 | 1 | 2021 | Overview of the Eighth Dialog System Technology Challenge: DSTC8 · IEEE ACM Trans. Audio Speech Lang. Process. 2021 |
Natural language and speech › Question answering and dialogue systems › multimodal dialogue system
audio-visual scene-aware dialog |
0.1 | 1 | 2021 | Overview of the Eighth Dialog System Technology Challenge: DSTC8 · IEEE ACM Trans. Audio Speech Lang. Process. 2021 |
Methods — techniques the papers use, named apart from their topics
end-to-end dialog modeling · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Overview of the Eighth Dialog System Technology Challenge: DSTC8abstractThis 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, baselines 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. Seokhwan Kim, Michel Galley, R. Chulaka Gunasekara, Adam Atkinson, Baolin Peng, Hannes Schulz, Jianfeng Gao 0001, Jinchao Li, Mahmoud Adada, Minlie Huang, Luis A. Lastras, Jonathan K. Kummerfeld, Walter S. Lasecki, Chiori Hori, Anoop Cherian, Tim K. Marks, Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara |
IEEE ACM Trans. Audio Speech Lang. Process. | 10 |