Autism Spectrum Disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, yet it could he complicated by several factors. Standard tests typically require intensive efforts and experience, which calls for developing assistive tools. In this respect, this study aims to develop a Machine Learning-based approach to assist the diagnosis process. Our approach is based on learning the sequence-based patterns in the saccadic eye movements. The key idea is to represent eye-tracking records as textual strings describing the sequences of fixations and saccades. As such, the study could borrow Natural Language Processing (NIP) methods for transforming the raw eye-tracking data. The NLP-based transformation could yield interesting features for training classification models. The experimental results demonstrated that such representation could be beneficial in this regard. With standard ConvNet models, our approach could realize a promising accuracy of classification (ROC-AUC up to 0.84).