Abstract
We show how eye-tracking corpora can be used to improve sentence compression models, presenting a novel multi-task learning algorithm based on multi-layer LSTMs. We obtain performance competitive with or better than state-of-the-art approaches.
Original language | English |
---|---|
Title of host publication | 2016 Conference of the North American Chapter of the Association for Computational Linguistics |
Subtitle of host publication | Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1528-1533 |
Number of pages | 6 |
ISBN (Electronic) | 9781941643914 |
DOIs | |
State | Published - 2016 |
Event | 15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - San Diego, United States Duration: 12 Jun 2016 → 17 Jun 2016 |
Publication series
Name | 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference |
---|
Conference
Conference | 15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 |
---|---|
Country/Territory | United States |
City | San Diego |
Period | 12/06/16 → 17/06/16 |
Bibliographical note
Publisher Copyright:©2016 Association for Computational Linguistics.
Funding
Yoav Goldberg was supported by the Israeli Science Foundation Grant No. 1555/15. Anders Søgaard was supported by ERC Starting Grant No. 313695. Thanks to Joachim Bingel and Maria Barrett for preparing data and for helpful discussions, and to the anonymous reviewers for their suggestions for improving the paper.
Funders | Funder number |
---|---|
European Commission | 313695 |
Israel Science Foundation | 1555/15 |