Demo Data from SIGIR 2026 / reviewers in the wild / expert
Prashant Jayannavar
dblp:185/6805
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 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
2 papers |
Language models and text generation · 45% Question answering and dialogue systems · 42% Vision and language · 7% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation
instruction following |
0.4 | 1 | 2020 | Learning to execute instructions in a Minecraft dialogue · ACL 2020 |
Natural language and speech › Question answering and dialogue systems
collaborative dialogue |
0.4 | 1 | 2019 | Collaborative Dialogue in Minecraft · ACL (1) 2019 |
Natural language and speech › Question answering and dialogue systems
dialogue generation |
0.4 | 1 | 2019 | Collaborative Dialogue in Minecraft · ACL (1) 2019 |
Natural language and speech › Language models and text generation › instruction tuning
instruction generation |
0.4 | 1 | 2019 | Collaborative Dialogue in Minecraft · ACL (1) 2019 |
Computer vision › Vision and language
grounded language understanding |
0.1 | 1 | 2020 | Learning to execute instructions in a Minecraft dialogue · ACL 2020 |
Knowledge, reasoning and agents › Multi-agent systems › human-agent interaction
interactive agents |
0.1 | 1 | 2019 | Collaborative Dialogue in Minecraft · ACL (1) 2019 |
Methods — techniques the papers use, named apart from their topics
sequence prediction · 0.4corpus annotation · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Learning to execute instructions in a Minecraft dialogueabstractThe Minecraft Collaborative Building Task is a two-player game in which an Architect A instructs a Builder B to construct a target structure out of 3D blocks.We consider the task of predicting B's action sequences (block placements and removals) in a given game context, and show that capturing B's past actions as well as B's perspective leads to a significant improvement in performance on this challenging language understanding problem. Prashant Jayannavar, Anjali Narayan-Chen, Julia Hockenmaier |
ACL | 1 |
| 2019 | Collaborative Dialogue in MinecraftabstractWe wish to develop interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios.Since computer games allow us to simulate such tasks without the need for physical robots, we define a Minecraft-based collaborative building task in which one player (A, the Architect) is shown a target structure and needs to instruct the other player (B, the Builder) to build this structure.Both players interact via a chat interface.A can observe B but cannot place blocks.We present the Minecraft Dialogue Corpus, a collection of 509 conversations and game logs.As a first step towards our goal of developing fully interactive agents for this task, we consider the subtask of Architect utterance generation, and show how challenging it is. Anjali Narayan-Chen, Prashant Jayannavar, Julia Hockenmaier |
ACL (1) | 2 |