**Abstr
act**

Language is not only about the words\, it is also abo ut the people. While much of the work in computational linguistics has foc used almost exclusively on words (and their relations)\, recent research i n the emerging field of computational sociolinguistics has shown that we c an effectively leverage the close interplay between language and people. I n this talk\, I will explore this interaction\, and show (1) that we can d evelop cross-cultural language models to identify words that are used in s ignificantly different ways by speakers from different cultures\; and (2) that we can effectively use information about the people behind the words to build better language representations.

\n**Biography**

Rada Mihalcea is a Professor of Computer Science and Engineerin g at the University of Michigan and the Director of the Michigan Artificia l Intelligence Lab. Her research interests are in computational linguistic s\, with a focus on lexical semantics\, multilingual natural language proc essing\, and computational social sciences. She serves or has served on th e editorial boards of the Journals of Computational Linguistics\, Language Resources and Evaluations\, Natural Language Engineering\, Journal of Art ificial Intelligence Research\, IEEE Transactions on Affective Computing\, and Transactions of the Association for Computational Linguistics. She w as a program co-chair for EMNLP 2009 and ACL 2011\, and a general chair fo r NAACL 2015 and *SEM 2019. She currently serves as ACL Vice-President. Sh e is the recipient of a Presidential Early Career Award for Scientists and Engineers awarded by President Obama (2009) and she is an ACM Fellow (201 9). In 2013\, she was made an honorary citizen of her hometown of Cluj-Nap oca\, Romania.

\n X-TAGS;LANGUAGE=en-US:2020\,February\,Mihalcea END:VEVENT BEGIN:VEVENT UID:ai1ec-22395@www.clsp.jhu.edu DTSTAMP:20230129T183215Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRecursive calls over recursive data are widely useful for generating probability distributions\, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful\, but unfortunately\, ex isting probabilistic programming languages do not perform exact inference on recursive calls over recursive data\, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wi de variety of recursion can be expressed naturally\, and inference carried out exactly. For instance\, probabilistic pushdown automata and their gen eralizations are easy to express\, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types usin g program transformations related to defunctionalization and refunctionali zation. These transformations are assured correct by a linear type system\ , and a successful choice of transformations\, if there is one\, is guaran teed to be found by a greedy algorithm. I will also describe the implement ation of this language in two phases: first\, compilation to a factor grap h grammar\, and second\, computing the sum-product of the factor graph gra mmar.\n\nBiography\nDavid Chiang (PhD\, University of Pennsylvania\, 2004) is an associate professor in the Department of Computer Science and Engin eering at the University of Notre Dame. His research is on computational m odels for learning human languages\, particularly how to translate from on e language to another. His work on applying formal grammars and machine le arning to translation has been recognized with two best paper awards (at A CL 2005 and NAACL HLT 2009). He has received research grants from DARPA\, NSF\, Google\, and Amazon\, has served on the executive board of NAACL and the editorial board of Computational Linguistics and JAIR\, and is curren tly on the editorial board of Transactions of the ACL. DTSTART;TZID=America/New_York:20221017T120000 DTEND;TZID=America/New_York:20221017T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:David Chiang (University of Notre Dame) “Exact Recursive Probabilis tic Programming with Colin McDonald\, Darcey Riley\, Kenneth Sible (Notre Dame) and Chung-chieh Shan (Indiana)” URL:https://www.clsp.jhu.edu/events/david-chiang-university-of-notre-dame/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Abstr
act**

Recursive calls over recursive data are w
idely useful for generating probability distributions\, and probabilistic
programming allows computations over these distributions to be expressed i
n a modular and intuitive way. Exact inference is also useful\, but unfort
unately\, existing probabilistic programming languages do not perform exac
t inference on recursive calls over recursive data\, forcing programmers t
o code many applications manually. We introduce a probabilistic language i
n which a wide variety of recursion can be expressed naturally\, and infer
ence carried out exactly. For instance\, probabilistic pushdown automata a
nd their generalizations are easy to express\, and polynomial-time parsing
algorithms for them are derived automatically. We eliminate recursive dat
a types using program transformations related to defunctionalization and r
efunctionalization. These transformations are assured correct by a linear
type system\, and a successful choice of transformations\, if there is one
\, is guaranteed to be found by a greedy algorithm. I will also describe t
he implementation of this language in two phases: first\, compilation to a
factor graph grammar\, and second\, computing the sum-product of the fact
or graph grammar.

\n\nDavid Chiang (PhD\,
University of Pennsylvania\, 2004) is an associate professor in the Depart
ment of Computer Science and Engineering at the University of Notre Dame.
His research is on computational models for learning human languages\, par
ticularly how to translate from one language to another. His work on apply
ing formal grammars and machine learning to translation has been recognize
d with two best paper awards (at ACL 2005 and NAACL HLT 2009). He has rece
ived research grants from DARPA\, NSF\, Google\, and Amazon\, has served o
n the executive board of NAACL and the editorial board of Computational Li
nguistics and JAIR\, and is currently on the editorial board of Transactio
ns of the ACL.

\n
X-TAGS;LANGUAGE=en-US:2022\,Chiang\,October
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