Dan Liebling
Dan Liebling (he/him) creates experiences that advance scientific research by integrating AI and academic knowledge. He joined the Science AI team in 2022 after five years of building and leading a research and engineering team focused on speech-to-speech translation experiences. His work at Google Research brings a human-computer interaction (HCI) lens to language-focused disciplines such as academic writing, speech recognition, and machine translation research. Prior to working at Google, he worked on information retrieval and human HCI research at Microsoft Research.
MS, Computer Science and Engineering, University of Washington
BS, Engineering and Applied Science, Caltech
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Authored Publications
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Three Directions for the Design of Human-Centered Machine Translation
Samantha Robertson
Wesley Deng
Timnit Gebru
Margaret Mitchell
Samy Bengio
Niloufar Salehi
(2021)
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As people all over the world adopt machine translation (MT) to communicate across languages, there is increased need for affordances that aid users in understanding when to rely on automated translations. Identifying the information and interactions that will most help users meet their translation needs is an open area of research at the intersection of Human-Computer Interaction (HCI) and Natural Language Processing (NLP). This paper advances work in this area by drawing on a survey of users' strategies in assessing translations. We identify three directions for the design of translation systems that support more reliable and effective use of machine translation: helping users craft good inputs, helping users understand translations, and expanding interactivity and adaptivity. We describe how these can be introduced in current MT systems and highlight open questions for HCI and NLP research.
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Unmet Needs and Opportunities for Mobile Translation AI
Abigail Evans
Aaron Michael Donsbach
Boris Smus
Jess Scon Holbrook
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20), ACM, Honolulu, Hawaii, USA
Preview abstract
Translation apps and devices are often presented in the context of providing assistance while traveling abroad. However, the spectrum of needs for cross-language communication is much wider. To investigate these needs, we conducted three studies with populations spanning socioeconomic status and geographic regions: (1) United States-based travelers, (2) migrant workers in India, and (3) immigrant populations in the United States. We compare frequent travelers' perception and actual translation needs with those of the two migrant communities. The latter two, with low language proficiency, have the greatest translation needs to navigate their daily lives. However, current mobile translation apps do not meet these needs. Our findings provide new insights on the usage practices and limitations of mobile translation tools. Finally, we propose design implications to help apps better serve these unmet needs.
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