Improving Cross-lingual Transfer Learning for Turkish NLP
John Doe, Jane Smith, Alex Johnson
ACL 2023
Abstract
This paper presents a novel approach to improve cross-lingual transfer learning for Turkish natural language processing tasks. We demonstrate significant improvements in performance across multiple NLP tasks including named entity recognition, part-of-speech tagging, and sentiment analysis. Our method leverages morphological information specific to Turkish to enhance the transfer of knowledge from high-resource languages.
BibTeX
@inproceedings{doe2023improving,
title={Improving Cross-lingual Transfer Learning for Turkish NLP},
author={Doe, John and Smith, Jane and Johnson, Alex},
booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics},
year={2023}
}
Introduction
Cross-lingual transfer learning has emerged as a promising approach for improving natural language processing (NLP) performance in low-resource languages. However, languages with rich morphology, such as Turkish, present unique challenges for these methods. In this paper, we address these challenges by introducing a novel approach that explicitly incorporates morphological information into the transfer learning process.
Method
Our approach consists of three main components:
- A morphological analyzer specifically designed for Turkish
- A modified pre-training objective that accounts for morphological structures
- A cross-lingual alignment method that maps between morphologically rich and poor languages
Results
Our experiments demonstrate significant improvements over previous state-of-the-art methods:
| Task | Previous SOTA | Our Method | Improvement |
|---|---|---|---|
| NER | 78.2% | 83.5% | +5.3% |
| POS | 92.1% | 94.7% | +2.6% |
| SA | 76.8% | 81.2% | +4.4% |
Conclusion
The results demonstrate that explicitly modeling morphological information can substantially improve cross-lingual transfer learning for Turkish NLP tasks. Our approach can be extended to other morphologically rich languages, potentially benefiting a wide range of low-resource language processing scenarios.