Demo Data from SIGIR 2026 / reviewers in the wild / expert
Malte Gabsdil
dblp:03/6805
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author
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 |
Speech recognition and synthesis · 33% Question answering and dialogue systems · 33% Image recognition and object detection · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Speech recognition and synthesis
automatic speech recognition |
0.0 | 1 | 2004 | Combining Acoustic and Pragmatic Features to Predict Recognition Performance in Spoken Dialogue Systems · ACL 2004 |
Computer vision › Image recognition and object detection › object recognition
context-based recognition |
0.0 | 1 | 2004 | Combining Acoustic and Pragmatic Features to Predict Recognition Performance in Spoken Dialogue Systems · ACL 2004 |
Natural language and speech › Question answering and dialogue systems
spoken dialogue systems |
0.0 | 1 | 2004 | Combining Acoustic and Pragmatic Features to Predict Recognition Performance in Spoken Dialogue Systems · ACL 2004 |
Methods — techniques the papers use, named apart from their topics
pragmatic plausibility features · 0.0machine learning · 0.0acoustic confidence features · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Combining Acoustic and Pragmatic Features to Predict Recognition Performance in Spoken Dialogue SystemsabstractWe use machine learners trained on a combination of acoustic confidence and pragmatic plausibility features computed from dialogue context to predict the accuracy of incoming n-best recognition hypotheses to a spoken dialogue system. Our best results show a 25% weighted f-score improvement over a baseline system that implements a "grammar-switching" approach to context-sensitive speech recognition. Malte Gabsdil, Oliver Lemon |
ACL | 1 |
| 2002 | Natural Language and Inference in a Computer Game
Malte Gabsdil, Alexander Koller, Kristina Striegnitz |
COLING | 1 |