Tuning language representation models for classification of Turkish news

This is an accepted manuscript of a paper published by ACM in 2021 International Symposium on Electrical, Electronics and Information Engineering proceedings on 19/02/2021, available online: https://doi.org/10.1145/3459104.3459170 The accepted manuscript of the publication may differ from the final published version.Pre-trained language representation models are very efficient in learning language representation independent from natural language processing tasks to be performed. The language representation models such as BERT and DistilBERT have achieved amazing results in many language understanding tasks. Studies on text classification problems in the literature are generally carried out for the English language. This study aims to classify the news in the Turkish language using pre-trained language representation models. In this study, we utilize BERT and DistilBERT by tuning both models for the text classification task to learn the categories of Turkish news with different tokenization methods. We provide a quantitative analysis of the performance of BERT and DistilBERT on the Turkish news dataset by comparing the models in terms of their representation capability in the text classification task. The highest performance is obtained with DistilBERT with an accuracy of 97.4%

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201