Eli Pincus


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)


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.


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.


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


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