CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities

Yiyun Liang, Zhucheng Tu, Laetitia Huang, Jimmy Lin

Abstract
We demonstrate a JavaScript implementation of a convolutional neural network that performs feedforward inference completely in the browser. Such a deployment means that models can run completely on the client, on a wide range of devices, without making backend server requests. This design is useful for applications with stringent latency requirements or low connectivity. Our evaluations show the feasibility of JavaScript as a deployment target. Furthermore, an in-browser implementation enables seamless integration with the JavaScript ecosystem for information visualization, providing opportunities to visually inspect neural networks and better understand their inner workings.
Anthology ID:
N18-5013
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Yang Liu, Tim Paek, Manasi Patwardhan
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
61–65
Language:
URL:
https://aclanthology.org/N18-5013
DOI:
10.18653/v1/N18-5013
Bibkey:
Cite (ACL):
Yiyun Liang, Zhucheng Tu, Laetitia Huang, and Jimmy Lin. 2018. CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 61–65, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities (Liang et al., NAACL 2018)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/N18-5013.pdf
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