Sorokin, Daniil ; Gurevych, Iryna (2017)
End-to-end Representation Learning for Question Answering with Weak Supervision.
Portoroz, Slovenia
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this paper we present a factoid question answering system for participation in Task 4 of the QALD-7 shared task. Our system is an end-to-end neural architecture for learning a semantic representation of the input question. It iteratively generates representations and uses a convolutional neural network (CNN) model to score them at each step. We take the semantic representation with the highest final score and execute it against Wikidata to retrieve the answers. We show on the Task 4 data set that our system is able to successfully generalize to new data.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Sorokin, Daniil ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | End-to-end Representation Learning for Question Answering with Weak Supervision |
| Sprache: | Englisch |
| Publikationsjahr: | Oktober 2017 |
| Verlag: | Springer, Cham |
| Buchtitel: | Semantic Web Challenges: 4th SemWebEval Challenge at ESWC 2017 |
| Reihe: | Communications in Computer and Information Science |
| Band einer Reihe: | 769 |
| Veranstaltungsort: | Portoroz, Slovenia |
| URL / URN: | https://link.springer.com/chapter/10.1007%2F978-3-319-69146-... |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | In this paper we present a factoid question answering system for participation in Task 4 of the QALD-7 shared task. Our system is an end-to-end neural architecture for learning a semantic representation of the input question. It iteratively generates representations and uses a convolutional neural network (CNN) model to score them at each step. We take the semantic representation with the highest final score and execute it against Wikidata to retrieve the answers. We show on the Task 4 data set that our system is able to successfully generalize to new data. |
| Freie Schlagworte: | UKP_reviewed;UKP_p_QAEduInf;reviewed;UKP_a_DLinNLP;UKP_a_LSRA |
| ID-Nummer: | TUD-CS-2017-0113 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 23 Mai 2017 16:26 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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