All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages

Barbara Plank

Abstract
We present All-In-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer feedback analysis in four languages: English, French, Japanese and Spanish.
Anthology ID:
I17-4024
Volume:
Proceedings of the IJCNLP 2017, Shared Tasks
Month:
December
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
143–148
Language:
URL:
https://aclanthology.org/I17-4024
DOI:
Bibkey:
Cite (ACL):
Barbara Plank. 2017. All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 143–148, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages (Plank, IJCNLP 2017)
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