Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language

Viktoriia Chekalina, Alexander Bondarenko, Chris Biemann, Meriem Beloucif, Varvara Logacheva, Alexander Panchenko


Abstract
We present a system for answering comparative questions (Is X better than Y with respect to Z?) in natural language. Answering such questions is important for assisting humans in making informed decisions. The key component of our system is a natural language interface for comparative QA that can be used in personal assistants, chatbots, and similar NLP devices. Comparative QA is a challenging NLP task, since it requires collecting support evidence from many different sources, and direct comparisons of rare objects may be not available even on the entire Web. We take the first step towards a solution for such a task offering a testbed for comparative QA in natural language by probing several methods, making the three best ones available as an online demo.
Anthology ID:
2021.eacl-demos.36
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
Month:
April
Year:
2021
Address:
Online
Editors:
Dimitra Gkatzia, Djamé Seddah
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
302–311
Language:
URL:
https://aclanthology.org/2021.eacl-demos.36
DOI:
10.18653/v1/2021.eacl-demos.36
Bibkey:
Cite (ACL):
Viktoriia Chekalina, Alexander Bondarenko, Chris Biemann, Meriem Beloucif, Varvara Logacheva, and Alexander Panchenko. 2021. Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 302–311, Online. Association for Computational Linguistics.
Cite (Informal):
Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language (Chekalina et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-demos.36.pdf