Jack the Reader – A Machine Reading Framework

Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, Sebastian Riedel

Abstract
Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions. Providing a set of useful primitives operating in a single framework of related tasks would allow for expressive modelling, and easier model comparison and replication. To that end, we present Jack the Reader (JACK), a framework for Machine Reading that allows for quick model prototyping by component reuse, evaluation of new models on existing datasets as well as integrating new datasets and applying them on a growing set of implemented baseline models. JACK is currently supporting (but not limited to) three tasks: Question Answering, Natural Language Inference, and Link Prediction. It is developed with the aim of increasing research efficiency and code reuse.
Anthology ID:
P18-4005
Volume:
Proceedings of ACL 2018, System Demonstrations
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Fei Liu, Thamar Solorio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
25–30
Language:
URL:
https://aclanthology.org/P18-4005
DOI:
10.18653/v1/P18-4005
Bibkey:
Cite (ACL):
Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, and Sebastian Riedel. 2018. Jack the Reader – A Machine Reading Framework. In Proceedings of ACL 2018, System Demonstrations, pages 25–30, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Jack the Reader – A Machine Reading Framework (Weissenborn et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/add_acl24_videos/P18-4005.pdf
Code
 uclmr/jack
Data
FB15k-237, MultiNLI, NewsQA, SNLI, SQuAD, TriviaQA
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