Eli Pincus


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Chahta Anumpa: A multimodal corpus of the Choctaw Language
Jacqueline Brixey | Eli Pincus | Ron Artstein
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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DialPort, Gone Live: An Update After A Year of Development
Kyusong Lee | Tiancheng Zhao | Yulun Du | Edward Cai | Allen Lu | Eli Pincus | David Traum | Stefan Ultes | Lina M. Rojas-Barahona | Milica Gasic | Steve Young | Maxine Eskenazi
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

DialPort collects user data for connected spoken dialog systems. At present six systems are linked to a central portal that directs the user to the applicable system and suggests systems that the user may be interested in. User data has started to flow into the system.


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Towards Automatic Identification of Effective Clues for Team Word-Guessing Games
Eli Pincus | David Traum
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Team word-guessing games where one player, the clue-giver, gives clues attempting to elicit a target-word from another player, the receiver, are a popular form of entertainment and also used for educational purposes. Creating an engaging computational agent capable of emulating a talented human clue-giver in a timed word-guessing game depends on the ability to provide effective clues (clues able to elicit a correct guess from a human receiver). There are many available web resources and databases that can be mined for the raw material for clues for target-words; however, a large number of those clues are unlikely to be able to elicit a correct guess from a human guesser. In this paper, we propose a method for automatically filtering a clue corpus for effective clues for an arbitrary target-word from a larger set of potential clues, using machine learning on a set of features of the clues, including point-wise mutual information between a clue’s constituent words and a clue’s target-word. The results of the experiments significantly improve the average clue quality over previous approaches, and bring quality rates in-line with measures of human clue quality derived from a corpus of human-human interactions. The paper also introduces the data used to develop this method; audio recordings of people making guesses after having heard the clues being spoken by a synthesized voice.


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Which Synthetic Voice Should I Choose for an Evocative Task?
Eli Pincus | Kallirroi Georgila | David Traum
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue


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Exploring Features For Localized Detection of Speech Recognition Errors
Eli Pincus | Svetlana Stoyanchev | Julia Hirschberg
Proceedings of the SIGDIAL 2013 Conference