This work represents one of the first efforts to bring explainability into Persian language QA systems, leveraging the powerful BERT model.
Overview of BERT
BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking model developed by Google for natural language understanding tasks. It is designed to understand the context of words in a sentence by looking at the words before and after them. BERT has revolutionized the field of NLP because it is pre-trained on a vast amount of text and can be fine-tuned for specific tasks like question answering, sentiment analysis, and more.
Overview of Question Answering Systems
Question Answering (QA) systems are a type of AI that can automatically answer questions posed by humans in natural language. These systems typically involve two main components:
Contextual Understanding: The model comprehends the context of the input text (e.g., a paragraph).
Answer Extraction: The model identifies and extracts the relevant answer from the context based on the given question.
In the context of Persian language processing, building such systems poses unique challenges due to the complexity of the language, as well as the limited availability of high-quality annotated data.
Explainable Persian QA with BERT
In this project, i implemented a Persian QA system using BERT, specifically tailored for the Persian language. What sets this work apart is the focus on explainability—ensuring that the model’s decisions and answer predictions are transparent and understandable to users. This is particularly important in sensitive applications where understanding the reasoning behind an AI’s response is crucial.
Access the Code
The code for this model, along with detailed documentation, is available on GitHub. he repository includes all the necessary resources for implementing the explainable Persian QA system.
Presentation at ICCKE 2022
This project was presented at the International Conference on Computer and Knowledge Engineering (ICCKE) in 2022. The conference provided an excellent platform to share and discuss cutting-edge research with peers from around the world.
If you are interested in a more in-depth explanation, including the technical aspects of the model, you can watch the video of my presentation, conducted in Persian: