The Impact of Multi-Domain Adaptation on the Quality of Classification of Free-Form Answers

The main aim of this dissertation is to investigate the impact of multi-domain adaptation on the quality of classification of free-form written responses. In modern natural language processing (NLP) tasks, precise classification of texts that differ in style, tone, and context is often required, making the task of multi-domain adaptation particularly relevant. Main research objectives: 1. Analysis of existing adaptation methods: Examine existing approaches to adapting machine learning models to different domains, including domain and inter-domain adaptation methods. 2. Development or application of methodology: Develop a new or apply an existing method of multi-domain adaptation that improves the classification of free-form texts. 3. Comparative analysis: Compare the proposed method with existing approaches based on standard quality metrics. 4. Experimental verification: Apply the developed method to real-world text classification tasks in areas such as customer response verification systems and review analysis. Research methodology: - Data collection and preparation: Collect textual data from various domains to create a multi-domain corpus. - Model adaptation: Apply and test various adaptation strategies, including transfer learning and adaptation using hidden states. - Evaluation and analysis: Assess the effectiveness of the proposed methods based on experimental data, analyze the obtained results, and interpret them. Expected results: It is expected that the applied multi-domain adaptation method will improve the quality of classification of free-form texts by more effectively utilizing information from various domains. This, in turn, will increase the accuracy and reliability of text analysis systems and find applications in natural language processing practices.