DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction

Heike Adel, Laura Ana Maria Bostan, Sean Papay, Sebastian Padó, Roman Klinger

Abstract
Most machine learning systems for natural language processing are tailored to specific tasks. As a result, comparability of models across tasks is missing and their applicability to new tasks is limited. This affects end users without machine learning experience as well as model developers. To address these limitations, we present DERE, a novel framework for declarative specification and compilation of template-based information extraction. It uses a generic specification language for the task and for data annotations in terms of spans and frames. This formalism enables the representation of a large variety of natural language processing challenges. The backend can be instantiated by different models, following different paradigms. The clear separation of frame specification and model backend will ease the implementation of new models and the evaluation of different models across different tasks. Furthermore, it simplifies transfer learning, joint learning across tasks and/or domains as well as the assessment of model generalizability. DERE is available as open-source software.
Anthology ID:
D18-2008
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2018
Address:
Brussels, Belgium
Editors:
Eduardo Blanco, Wei Lu
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
42–47
Language:
URL:
https://aclanthology.org/D18-2008
DOI:
10.18653/v1/D18-2008
Bibkey:
Cite (ACL):
Heike Adel, Laura Ana Maria Bostan, Sean Papay, Sebastian Padó, and Roman Klinger. 2018. DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 42–47, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction (Adel et al., EMNLP 2018)
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https://preview.aclanthology.org/nschneid-patch-2/D18-2008.pdf
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