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
- Editors:
- Chao-Hong Liu, Preslav Nakov, Nianwen Xue
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 143–148
- Language:
- URL:
- https://aclanthology.org/I17-4024
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/I17-4024.pdf