Abstract
This article presents the model that generated the runs submitted by the R2I_LIS team to the VarDial2019 evaluation campaign, more particularly, to the binary classification by dialect sub-task of the Moldavian vs. Romanian Cross-dialect Topic identification (MRC) task. The team proposed a majority vote-based model, between five supervised machine learning models, trained on forty manually-crafted features. One of the three submitted runs was ranked second at the binary classification sub-task, with a performance of 0.7963, in terms of macro-F1 measure. The other two runs were ranked third and fourth, respectively.- Anthology ID:
- W19-1414
- Volume:
- Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
- Month:
- June
- Year:
- 2019
- Address:
- Ann Arbor, Michigan
- Editors:
- Marcos Zampieri, Preslav Nakov, Shervin Malmasi, Nikola Ljubešić, Jörg Tiedemann, Ahmed Ali
- Venue:
- VarDial
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 138–143
- Language:
- URL:
- https://aclanthology.org/W19-1414
- DOI:
- 10.18653/v1/W19-1414
- Cite (ACL):
- Adrian-Gabriel Chifu. 2019. The R2I_LIS Team Proposes Majority Vote for VarDial’s MRC Task. In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 138–143, Ann Arbor, Michigan. Association for Computational Linguistics.
- Cite (Informal):
- The R2I_LIS Team Proposes Majority Vote for VarDial’s MRC Task (Chifu, VarDial 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W19-1414.pdf
- Data
- MOROCO