TY - GEN
T1 - Incremental prediction of sentence-final verbs
T2 - 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016
AU - Grissom, Alvin C.
AU - Orita, Naho
AU - Boyd-Graber, Jordan
N1 - Funding Information:
We would like to thank the anonymous reviewers for their comments. We thank Yusuke Miyao for his helpful support. We would also like to thank James H. Martin, Martha Palmer, Hal Daumé III, Mans Hulden, Mohit Iyyer, John Morgan, Shota Momma, Graham Neubig, and Sho Hoshino for their invaluable discussions and input. This work was supported by NSF grant IIS-1320538. Boyd-Graber is also partially supported by NSF grants CCF-1409287 and NCSE-1422492. Any opinions, findings, conclusions, or recommendations expressed here are those of the authors and do not necessarily reflect the view of the sponsor.
Funding Information:
We would like to thank the anonymous reviewers for their comments. We thank Yusuke Miyao for his helpful support. We would also like to thank James H. Martin, Martha Palmer, Hal Daum? III, Mans Hulden, Mohit Iyyer, John Morgan, Shota Momma, Graham Neubig, and Sho Hoshino for their invaluable discussions and input. This work was supported by NSF grant IIS-1320538. Boyd-Graber is also partially supported by NSF grants CCF-1409287 and NCSE-1422492. Any opinions, findings, conclusions, or recommendations expressed here are those of the authors and do not necessarily reflect the view of the sponsor.
Publisher Copyright:
© 2016 Association for Computational Linguistics.
PY - 2016
Y1 - 2016
N2 - Verb prediction is important in human sentence processing and, practically, in simultaneous machine translation. In verb-final languages, speakers select the final verb before it is uttered, and listeners predict it before it is uttered. Simultaneous interpreters must do the same to translate in real-time. Motivated by the problem of SOV-SVO simultaneous machine translation, we provide a study of incremental verb prediction in verb-final languages. As a basis of comparison, we examine incremental verb prediction with human participants in a multiple choice setting using crowdsourcing to gain insight into incremental human performance in a constrained setting. We then examine a computational approach to incremental verb prediction using discriminative classification with shallow features. Both humans and machines predict verbs more accurately as more of a sentence becomes available, and case markers—when available—help humans and sometimes machines predict final verbs.
AB - Verb prediction is important in human sentence processing and, practically, in simultaneous machine translation. In verb-final languages, speakers select the final verb before it is uttered, and listeners predict it before it is uttered. Simultaneous interpreters must do the same to translate in real-time. Motivated by the problem of SOV-SVO simultaneous machine translation, we provide a study of incremental verb prediction in verb-final languages. As a basis of comparison, we examine incremental verb prediction with human participants in a multiple choice setting using crowdsourcing to gain insight into incremental human performance in a constrained setting. We then examine a computational approach to incremental verb prediction using discriminative classification with shallow features. Both humans and machines predict verbs more accurately as more of a sentence becomes available, and case markers—when available—help humans and sometimes machines predict final verbs.
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M3 - Conference contribution
AN - SCOPUS:85072753641
T3 - CoNLL 2016 - 20th SIGNLL Conference on Computational Natural Language Learning, Proceedings
SP - 95
EP - 104
BT - CoNLL 2016 - 20th SIGNLL Conference on Computational Natural Language Learning, Proceedings
PB - Association for Computational Linguistics (ACL)
Y2 - 11 August 2016 through 12 August 2016
ER -