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David C. Hau

dblp:159/0540 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Human-AI interaction › conversational interaction
conversational grounding
0.212015
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
0.212015
Two is Better Than One: Improving Multilingual Collaboration by Giving Two Machine Translation Outputs · CSCW 2015
Collaborative and social computing › team collaboration
multilingual collaboration
0.212015
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
YearPublicationVenuePosition
2015 Two is Better Than One: Improving Multilingual Collaboration by Giving Two Machine Translation Outputs
abstract
Machine 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
CSCW3