InstructNet: A novel approach for multi-label instruction classification through advanced deep learning

• Published in: PloS one 2024-10, Vol.19 (10), p.e0311161
• Main Authors: Aurpa, Tanjim Taharat, Ahmed, Md Shoaib, Rahman, Md Mahbubur, Moazzam, Md Golam
• Format: Article
• Language: English
• Subjects: Accuracy; Classification; Computational linguistics; Datasets; Deep Learning; Effectiveness; Evaluation; Humans; Instruction execution (Computers); Knowledge; Knowledge bases (artificial intelligence); Labels; Language; Language processing; Large language models; Machine learning; Natural language interfaces; Natural language processing; Neural Networks, Computer; Performance evaluation; Performance measurement; Search engines; Text categorization; Transformers
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0311161

People use search engines for various topics and items, from daily essentials to more aspirational and specialized objects. Therefore, search engines have taken over as people's preferred resource. The "How To" prefix has become familiar and widely used in various search styles to find solutions to particular problems. This search allows people to find sequential instructions by providing detailed guidelines to accomplish specific tasks. Categorizing instructional text is also essential for task-oriented learning and creating knowledge bases. This study uses the "How To" articles to determine the multi-label instruction category. We have brought this work with a dataset comprising 11,121 observations from wikiHow, where each record has multiple categories. To find out the multi-label category meticulously, we employ some transformer-based deep neural architectures, such as Generalized Autoregressive Pretraining for Language Understanding (XLNet), Bidirectional Encoder Representation from Transformers (BERT), etc. In our multi-label instruction classification process, we have reckoned our proposed architectures using accuracy and macro f1-score as the performance metrics. This thorough evaluation showed us much about our strategy's strengths and drawbacks. Specifically, our implementation of the XLNet architecture has demonstrated unprecedented performance, achieving an accuracy of 97.30% and micro and macro average scores of 89.02% and 93%, a noteworthy accomplishment in multi-label classification. This high level of accuracy and macro average score is a testament to the effectiveness of the XLNet architecture in our proposed 'InstructNet' approach. By employing a multi-level strategy in our evaluation process, we have gained a more comprehensive knowledge of the effectiveness of our proposed architectures and identified areas for forthcoming improvement and refinement.