A Turkish Question Answering System Based on Deep Learning Neural Networks
Cavide Balkı GEMİRTER, Dionysis Goularas
Abstract
In the domain of Natural Language Processing (NLP), despite the progress made for some common languages, difficulties persist for many others for the completion of particular NLP tasks. In this scope, the current study aims to explore these challenges by proposing a question answering (QA) system in the Turkish language. In particular, the system will generate the best answers in terms of content and length from questions that are based on a set of documents related to the banking sector. In order to achieve this goal, the system utilizes advanced artificial intelligence algorithms and large data sets. More specifically, BERT algorithm is used for the generation of the language model, followed by a fine-tuning procedure for performing a machine reading for question answering (MRQA) task. In this work, various experiments were conducted using original and translated data sets in an effort to solve the challenges that arise from morphologically complex languages as Turkish. Finally, the system achieved a performance that overall is applicable to a wider range than any other QA system in the Turkish language. The proposed methodology is not only proper to the Turkish language, but can also be adapted to any other language for performing various NLP tasks.