BibTeX
@InProceedings{adel2018domainindependent,
address = {Brussels, Belgium},
author = {Adel, Heike and Bostan, Laura Ana Maria and Papay, Sean and Padó, Sebastian and Klinger, Roman},
booktitle = {Proceedings of EMNLP},
title = {DERE: A task and domain-independent slot filling framework for declarative relation extraction},
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.},
year = {2018},
url = {https://aclweb.org/anthology/D18-2008.pdf},
pages = {42-47}}