Comparison of Pre-trained Language Models for Turkish Address Parsing

TL;DR

This study evaluates various pre-trained language models for Turkish address parsing, demonstrating that Turkish-specific models with MLP fine-tuning outperform multilingual models, with visualizations supporting their effectiveness.

Contribution

It introduces a Turkish address parsing dataset and compares multilingual and Turkish-specific BERT models, proposing an MLP fine-tuning approach that improves performance.

Findings

Turkish-specific models with MLP fine-tuning outperform multilingual models.

Constructed a high-quality Turkish address parsing dataset.

Visualization confirms effectiveness of BERT variants in address classification.

Abstract

Transformer based pre-trained models such as BERT and its variants, which are trained on large corpora, have demonstrated tremendous success for natural language processing (NLP) tasks. Most of academic works are based on the English language; however, the number of multilingual and language specific studies increase steadily. Furthermore, several studies claimed that language specific models outperform multilingual models in various tasks. Therefore, the community tends to train or fine-tune the models for the language of their case study, specifically. In this paper, we focus on Turkish maps data and thoroughly evaluate both multilingual and Turkish based BERT, DistilBERT, ELECTRA and RoBERTa. Besides, we also propose a MultiLayer Perceptron (MLP) for fine-tuning BERT in addition to the standard approach of one-layer fine-tuning. For the dataset, a mid-sized Address Parsing corpus taken with a relatively high quality is constructed. Conducted experiments on this dataset indicate that Turkish language specific models with MLP fine-tuning yields slightly better results when compared to the multilingual fine-tuned models. Moreover, visualization of address tokens' representations further indicates the effectiveness of BERT variants for classifying a variety of addresses.