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In this paper, we present a unified model that works for both multilingualand crosslingual prediction of reading times of words in various languages. Thesecret behind the success of this model is in the preprocessing step where allwords are transformed to their universal language representation via theInternational Phonetic Alphabet (IPA). To the best of our knowledge, this isthe first study to favorable exploit this phonological property of language forthe two tasks. Various feature types were extracted covering basic frequencies,n-grams, information theoretic, and psycholinguistically-motivated predictorsfor model training. A finetuned Random Forest model obtained best performancefor both tasks with 3.8031 and 3.9065 MAE scores for mean first fixationduration (FFDAvg) and mean total reading time (TRTAvg) respectively.

NU HLT at CMCL 2022 Shared Task: Multilingual and Crosslingual Prediction of Human Reading Behavior in Universal Language Space