Exploring the State-of-the-Art Language Modeling Methods and Data Augmentation Techniques for Multilingual Clause-Level Morphology

• URL: http://arxiv.org/abs/2211.01736v1
• Date: Thu, 3 Nov 2022 11:53:39 GMT
• Title: Exploring the State-of-the-Art Language Modeling Methods and Data Augmentation Techniques for Multilingual Clause-Level Morphology
• Authors: Emre Can Acikgoz, Tilek Chubakov, M\"uge Kural, G\"ozde G\"ul \c{S}ahin, Deniz Yuret
• Abstract summary: We present our work on all three parts of the shared task: inflection, reinflection, and analysis. We mainly explore two approaches: Transformer models in combination with data augmentation, and exploiting the state-of-the-art language modeling techniques for morphological analysis. Our methods achieved first place in each of the three tasks and outperforms mT5-baseline with 89% for inflection, 80% for reinflection and 12% for analysis.
• Score: 3.8498574327875947
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes the KUIS-AI NLP team's submission for the 1$^{st}$ 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: Transformer models in combination with data augmentation, and exploiting the state-of-the-art language modeling techniques for morphological analysis. Data augmentation leads 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 reinflection 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 reinflection and ~12\% for analysis. Our code https://github.com/emrecanacikgoz/mrl2022 is publicly available.

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