Bugert, Michael ; Puzikov, Yevgeniy ; Rücklé, Andreas ; Eckle-Kohler, Judith ; Martin, Teresa ; Martínez Cámara, Eugenio ; Sorokin, Daniil ; Peyrard, Maxime ; Gurevych, Iryna (2017)
LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test.
The 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics. Valencia, Spain (03.04.2017--04.04.2017)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems. As part of the LSDSem 2017 shared task, we present a system based on a deep learning architecture combined with a rich set of manually-crafted linguistic features. The system outperforms all known baselines for the task, suggesting that the chosen approach is promising. We additionally present two methods for generating further training data based on stories from the ROCStories corpus. Our system and generated data are publicly available on GitHub.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Bugert, Michael ; Puzikov, Yevgeniy ; Rücklé, Andreas ; Eckle-Kohler, Judith ; Martin, Teresa ; Martínez Cámara, Eugenio ; Sorokin, Daniil ; Peyrard, Maxime ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test |
| Sprache: | Englisch |
| Publikationsjahr: | April 2017 |
| Verlag: | Association for Computational Linguistics |
| Buchtitel: | Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics |
| Veranstaltungstitel: | The 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics |
| Veranstaltungsort: | Valencia, Spain |
| Veranstaltungsdatum: | 03.04.2017--04.04.2017 |
| URL / URN: | http://aclweb.org/anthology/W17-0908 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems. As part of the LSDSem 2017 shared task, we present a system based on a deep learning architecture combined with a rich set of manually-crafted linguistic features. The system outperforms all known baselines for the task, suggesting that the chosen approach is promising. We additionally present two methods for generating further training data based on stories from the ROCStories corpus. Our system and generated data are publicly available on GitHub. |
| Freie Schlagworte: | UKP_p_DIP;UKP_p_QAEduInf;UKP_reviewed;UKP_a_DLinNLP;UKP_a_LSRA;AIPHES |
| ID-Nummer: | TUD-CS-2017-0040 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 21 Feb 2017 20:05 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
| PPN: | |
| Zugehörige Links: | |
| Projekte: | AIPHES, UKP_p_DIP, UKP_p_QAUduInf |
| Sponsoren: | German Research Foundation, grant No.GU 798/17-1, German Research Foundation, research training group “Adaptive Preparation of Information form Heterogeneous Sources” (AIPHES), GRK 1994/1, QA-EduInf project, grants No. GU 798/18-1 and No. RI 803/12-1 |
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