no code implementations • 8 Apr 2024 • Nanna Inie, Stefania Druga, Peter Zukerman, Emily M. Bender
We use a survey-based approach (n=954) to investigate whether participants are likely to trust one of two (fictitious) "AI" systems by randomly assigning people to see either an anthropomorphized or a de-anthropomorphized description of the systems.
no code implementations • 5 Dec 2023 • Marcel Binz, Stephan Alaniz, Adina Roskies, Balazs Aczel, Carl T. Bergstrom, Colin Allen, Daniel Schad, Dirk Wulff, Jevin D. West, Qiong Zhang, Richard M. Shiffrin, Samuel J. Gershman, Ven Popov, Emily M. Bender, Marco Marelli, Matthew M. Botvinick, Zeynep Akata, Eric Schulz
For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate.
no code implementations • 26 Nov 2021 • Inioluwa Deborah Raji, Emily M. Bender, Amandalynne Paullada, Emily Denton, Alex Hanna
There is a tendency across different subfields in AI to valorize a small collection of influential benchmarks.
no code implementations • 9 Dec 2020 • Amandalynne Paullada, Inioluwa Deborah Raji, Emily M. Bender, Emily Denton, Alex Hanna
Datasets have played a foundational role in the advancement of machine learning research.
no code implementations • ACL 2020 • Emily M. Bender, Dirk Hovy, Alex Schofield, ra
To raise awareness among future NLP practitioners and prevent inertia in the field, we need to place ethics in the curriculum for all NLP students{---}not as an elective, but as a core part of their education.
no code implementations • ACL 2020 • Emily M. Bender, Alex Koller, er
The success of the large neural language models on many NLP tasks is exciting.
no code implementations • NAACL 2019 • Haley Lepp, Olga Zamaraeva, Emily M. Bender
We present a web-based system that facilitates the exploration of complex morphological patterns found in morphologically very rich languages.
1 code implementation • NAACL 2019 • Valerie Hajdik, Jan Buys, Michael W. Goodman, Emily M. Bender
We propose neural models to generate high-quality text from structured representations based on Minimal Recursion Semantics (MRS).
no code implementations • ACL 2018 • Emily M. Bender
Meaning is a fundamental concept in Natural Language Processing (NLP), given its aim to build systems that mean what they say to you, and understand what you say to them.
Recommendation Systems Unsupervised Person Re-Identification
no code implementations • TACL 2018 • Emily M. Bender, Batya Friedman
In this paper, we propose data statements as a design solution and professional practice for natural language processing technologists, in both research and development.
no code implementations • WS 2017 • Allyson Ettinger, Sudha Rao, Hal Daumé III, Emily M. Bender
This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language Edition shared task.
no code implementations • WS 2017 • Gina-Anne Levow, Emily M. Bender, Patrick Littell, Kristen Howell, Shobhana Chelliah, Joshua Crowgey, Dan Garrette, Jeff Good, Sharon Hargus, David Inman, Michael Maxwell, Michael Tjalve, Fei Xia
no code implementations • LREC 2014 • Dan Flickinger, Emily M. Bender, Stephan Oepen
We motivate and describe the design and development of an emerging encyclopedia of compositional semantics, pursuing three objectives.
no code implementations • LREC 2014 • Emily M. Bender
Language CoLLAGE is a collection of grammatical descriptions developed in the context of a grammar engineering graduate course with the LinGO Grammar Matrix.
no code implementations • LREC 2014 • Fei Xia, William Lewis, Michael Wayne Goodman, Joshua Crowgey, Emily M. Bender
In this paper, we describe the expansion of the ODIN resource, a database containing many thousands of instances of Interlinear Glossed Text (IGT) for over a thousand languages harvested from scholarly linguistic papers posted to the Web.