@article{prabhakaran2018tacl,
 abstract = {We apply computational dialog methods to police
body-worn camera footage to model conversations between police officers and
community members in traffic stops. Relying on the theory of institutional
talk, we develop a labeling scheme for police speech during traffic stops, and a tagger to detect institutional dialog acts (Reasons, Searches, Offering Help) from transcribed text at the turn (78% F-score) and stop (89%
F-score) level. We then develop speech recognition and segmentation
algorithms to detect these acts at the stop level from raw camera audio
(81% F-score, with even higher accuracy for crucial acts like conveying the
reason for the stop). We demonstrate that the dialog structures produced by
our tagger could reveal whether officers follow law enforcement norms like
introducing themselves, explaining the reason for the stop, and asking
permission for searches. This work may therefore inform and aid efforts to
ensure the procedural justice of police-community interactions.},
 author = {Prabhakaran, Vinodkumar and Griffiths, Camilla and Su, Hang and Verma, Prateek and Morgan, Nelson and Eberhardt, Jennifer and
Jurafsky, Dan},
 issn = {2307-387X},
 journal = {Transactions of the Association for Computational
Linguistics},
 keyword = {},
 link = {https://transacl.org/ojs/index.php/tacl/article/view/1349},
 pages = {467--481},
 title = {Detecting Institutional Dialog Acts in Police Traffic
Stops},
 url = {https://nlp.stanford.edu/pubs/prabhakaran2018tacl.pdf},
 volume = {6},
 year = {2018}
}