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2022 ACL ACL 2022

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

Abstract

AbstractIn this paper, we present a unified model that works for both multilingual and crosslingual prediction of reading times of words in various languages. The secret behind the success of this model is in the preprocessing step where all words are transformed to their universal language representation via the International Phonetic Alphabet (IPA). To the best of our knowledge, this is the first study to favorably exploit this phonological property of language for the two tasks. Various feature types were extracted covering basic frequencies, n-grams, information theoretic, and psycholinguistically-motivated predictors for model training. A finetuned Random Forest model obtained best performance for both tasks with 3.8031 and 3.9065 MAE scores for mean first fixation duration (FFDAvg) and mean total reading time (TRTAvg) respectively.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Interdisciplinary and Machine Learning
🧭 Keyword Pioneer — multilingual prediction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Joseph Marvin Imperial

Topics

Machine Learning > Core Methods > Regression Data Science & Analytics > Methods > Time Series Analysis Interdisciplinary > Linguistics Machine Learning > Learning Types > Multi-Lingual Learning

Keywords

eye tracking gradient boosting random forest international phonetic alphabet phonological feature reading time crosslingual transfer psycholinguistic feature multilingual prediction psycholinguistic predictor
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