Abstract
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.- Anthology ID:
 - L16-1435
 - Volume:
 - Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
 - Month:
 - May
 - Year:
 - 2016
 - Address:
 - Portorož, Slovenia
 - Editors:
 - Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
 - Venue:
 - LREC
 - SIG:
 - Publisher:
 - European Language Resources Association (ELRA)
 - Note:
 - Pages:
 - 2741–2747
 - Language:
 - URL:
 - https://aclanthology.org/L16-1435
 - DOI:
 - Cite (ACL):
 - Eli Pincus and David Traum. 2016. Towards Automatic Identification of Effective Clues for Team Word-Guessing Games. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2741–2747, Portorož, Slovenia. European Language Resources Association (ELRA).
 - Cite (Informal):
 - Towards Automatic Identification of Effective Clues for Team Word-Guessing Games (Pincus & Traum, LREC 2016)
 - PDF:
 - https://preview.aclanthology.org/ingest-acl-2023-videos/L16-1435.pdf