Language users are remarkably good at making inferences about speakers' intentions in context, and children learning their native language also display substantial skill in acquiring the meanings of unknown words. These two cases are deeply related: Language users invent new terms in conversation, and language learners learn the literal meanings of words based on their pragmatic inferences about how those words are used. While pragmatic inference and word learning have both been independently characterized in probabilistic terms, no current work unifies these two. We describe a model in which language learners assume that they jointly approximate a shared, external lexicon and reason recursively about the goals of others in using this lexicon. This model captures phenomena in word learning and pragmatic inference; it additionally leads to insights about the emergence of communicative systems in conversation and the mechanisms by which pragmatic inferences become incorporated into word meanings.

The events of the past year have left different marks on everyone's lives. For some of us in Texas, these marks are especially deep. We have not only experienced the heartache of the COVID-19 pandemic but also the worst winter storm in almost a century. To say the least, the 2020–21 academic year was a very memorable one for all of us. Both the pandemic and big freeze -- and its subsequent statewide power and water outages -- disrupted our lives, but we adapted, both on and off campus.

MetroLab Network has partnered with Government Technology to bring its readers a segment called the MetroLab Innovation of the Month Series, which highlights impactful tech, data and innovation projects underway between cities and universities. If you'd like to learn more or contact the project leads, please contact MetroLab at info@metrolabnetwork.org for more information. In this month's installment of the Innovation of the Month series, we explore a collaboration between the University of Texas at Austin and the city of Austin, involving leveraging AI to improve the lives of people experiencing homelessness. MetroLab's Ben Levine spoke with Sherri R. Greenberg from the UT-Austin LBJ School of Public Affairs; Min Kyung Lee, Stephen C. Slota and Kenneth R. Fleischmann from the UT-Austin School of Information; James Snow from the city of Austin Public Works Department; and Jonathan Tomko from the city of Austin Neighborhood Housing and Community Development Department about the background and development of their project. Ben Levine: Can you describe the origin and objective of this project and who has been involved in it?

MetroLab Network has partnered with Government Technology to bring its readers a segment called the MetroLab Innovation of the Month Series, which highlights impactful tech, data and innovation projects underway between cities and universities. If you'd like to learn more or contact the project leads, please contact MetroLab at info@metrolabnetwork.org for more information. In this month's installment of the Innovation of the Month series, we explore a collaboration between the University of Texas at Austin and the city of Austin, involving leveraging AI to improve the lives of people experiencing homelessness. MetroLab's Ben Levine spoke with Sherri R. Greenberg from the UT-Austin LBJ School of Public Affairs; Min Kyung Lee, Stephen C. Slota and Kenneth R. Fleischmann from the UT-Austin School of Information; James Snow from the city of Austin Public Works Department; and Jonathan Tomko from the city of Austin Neighborhood Housing and Community Development Department about the background and development of their project. Ben Levine: Can you describe the origin and objective of this project and who has been involved in it?

Language users are remarkably good at making inferences about speakers' intentions in context, and children learning their native language also display substantial skill in acquiring the meanings of unknown words. These two cases are deeply related: Language users invent new terms in conversation, and language learners learn the literal meanings of words based on their pragmatic inferences about how those words are used. While pragmatic inference and word learning have both been independently characterized in probabilistic terms, no current work unifies these two. We describe a model in which language learners assume that they jointly approximate a shared, external lexicon and reason recursively about the goals of others in using this lexicon. This model captures phenomena in word learning and pragmatic inference; it additionally leads to insights about the emergence of communicative systems in conversation and the mechanisms by which pragmatic inferences become incorporated into word meanings.