Transformers on Multilingual Clause-Level Morphology
Emre Can Acikgoz, Tilek Chubakov, Muge Kural, Gözde Şahin, Deniz Yuret
Abstract
This paper describes the KUIS-AI NLP team’s submission for the 1st Shared Task on Multilingual Clause-level Morphology (MRL2022). We present our work on all three parts of the shared task: inflection, reinflection, and analysis. We mainly explore two approaches: Trans- former models in combination with data augmentation, and exploiting the state-of-the-art language modeling techniques for morphological analysis. Data augmentation leads to a remarkable performance improvement for most of the languages in the inflection task. Prefix-tuning on pretrained mGPT model helps us to adapt reinflection and analysis tasks in a low-data setting. Additionally, we used pipeline architectures using publicly available open-source lemmatization tools and monolingual BERT- based morphological feature classifiers for rein- flection and analysis tasks, respectively. While Transformer architectures with data augmentation and pipeline architectures achieved the best results for inflection and reinflection tasks, pipelines and prefix-tuning on mGPT received the highest results for the analysis task. Our methods achieved first place in each of the three tasks and outperforms mT5-baseline with 89% for inflection, 80% for reflection, and 12% for analysis. Our code 1 is publicly available.- Anthology ID:
- 2022.mrl-1.10
- Volume:
- Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Duygu Ataman, Hila Gonen, Sebastian Ruder, Orhan Firat, Gözde Gül Sahin, Jamshidbek Mirzakhalov
- Venue:
- MRL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 100–105
- Language:
- URL:
- https://aclanthology.org/2022.mrl-1.10
- DOI:
- 10.18653/v1/2022.mrl-1.10
- Cite (ACL):
- Emre Can Acikgoz, Tilek Chubakov, Muge Kural, Gözde Şahin, and Deniz Yuret. 2022. Transformers on Multilingual Clause-Level Morphology. In Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 100–105, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Transformers on Multilingual Clause-Level Morphology (Acikgoz et al., MRL 2022)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2022.mrl-1.10.pdf