Demo Data from SIGIR 2026 / reviewers in the wild / expert
David C. Hau
dblp:159/0540
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1
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.
| Human-computer interaction and pervasive computing
1 paper |
Collaborative and social computing · 67% Human-AI interaction · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction › conversational interaction
conversational grounding |
0.2 | 1 | 2015 | Two is Better Than One: Improving Multilingual Collaboration by Giving Two Machine Translation Outputs · CSCW 2015 |
Collaborative and social computing › team collaboration › multilingual collaboration
machine translation-mediated communication |
0.2 | 1 | 2015 | Two is Better Than One: Improving Multilingual Collaboration by Giving Two Machine Translation Outputs · CSCW 2015 |
Collaborative and social computing › team collaboration
multilingual collaboration |
0.2 | 1 | 2015 | Two is Better Than One: Improving Multilingual Collaboration by Giving Two Machine Translation Outputs · CSCW 2015 |
Methods — techniques the papers use, named apart from their topics
map navigation task · 0.2laboratory experiment · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Two is Better Than One: Improving Multilingual Collaboration by Giving Two Machine Translation OutputsabstractMachine translation (MT) creates both opportunities and challenges for multilingual collaboration: While MT enables collaborators to communicate via their native languages, it can introduce errors that make communication difficult. In the current paper, we examine whether displaying two alternative translations for each message will improve conversational grounding and task performance. We conducted a laboratory experiment in which monolingual native English speakers collaborated with bilingual native Mandarin speakers on a map navigation task. Each dyad performed the task in one of three communication conditions: MT with single output, MT with two outputs, and English as a common language. Dyads given two translations for each message communicated more efficiently, and performed better on the task, than dyads given one translation. Our findings show the value of providing multiple translations in multilingual collaboration, and suggest design features of future MT-based collaboration tools. Ge Gao 0001, Bin Xu 0002, David C. Hau, Dan Cosley, Susan R. Fussell |
CSCW | 3 |