Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Prashant Jayannavar

dblp:185/6805 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation
instruction following
0.412020
Learning to execute instructions in a Minecraft dialogue · ACL 2020
Natural language and speech › Question answering and dialogue systems
collaborative dialogue
0.412019
Collaborative Dialogue in Minecraft · ACL (1) 2019
Natural language and speech › Question answering and dialogue systems
dialogue generation
0.412019
Collaborative Dialogue in Minecraft · ACL (1) 2019
Natural language and speech › Language models and text generation › instruction tuning
instruction generation
0.412019
Collaborative Dialogue in Minecraft · ACL (1) 2019
Computer vision › Vision and language
grounded language understanding
0.112020
Learning to execute instructions in a Minecraft dialogue · ACL 2020
Knowledge, reasoning and agents › Multi-agent systems › human-agent interaction
interactive agents
0.112019
Collaborative Dialogue in Minecraft · ACL (1) 2019

Methods — techniques the papers use, named apart from their topics

sequence prediction · 0.4corpus annotation · 0.4
YearPublicationVenuePosition
2020 Learning to execute instructions in a Minecraft dialogue
abstract
The 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
ACL1
2019 Collaborative Dialogue in Minecraft
abstract
We 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