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Mild Cognitive Impairment (MCI) is considered a prodromal stage of dementia, including Alzheimer’s disease. It is characterized by behavioral changes and decreased cognitive function, while individuals can still maintain their independence. Early detection of MCI is critical, as it allows for timely intervention, enrichment of clinical trial cohorts, and the development of therapeutic approaches. Recently, language markers have been shown to be a promising approach to identifying MCI in a non-intrusive, affordable, and accessible fashion. In the InterSpeech 2024 TAUKADIAL Challenge, we study language markers from spontaneous speech in English and Chinese and use the bilingual language markers to identify MCI cases and predict the Mini-Mental Status Examination (MMSE) scores. Our proposed framework combines the power from 1) feature extraction of a comprehensive set of bilingual acoustic features, and semantic and syntactic features from language models; 2) careful treatment of model complexity for small sample size; 3) consideration of imbalanced demographic structure, potential outlier removal, and a multi-task treatment that uses the prediction of clinical classification as prior for MMSE prediction. The proposed approach delivers an average of 78.2% Balanced Accuracy in MCI detection and an averaged RMSE of 2.705 in predicting MMSE. Our empirical evaluation shows that translingual language markers can improve the detection of MCI from spontaneous speech. Our codes are provided in https://github.com/illidanlab/translingual-language-markers.