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

Joseph Marvin Imperial

Keywords: eye-tracking, multilingual, crosslingual, reading, features

TL;DR: Converting eye-tracking data to its IPA equivalent allows a single solution for multilingual and crosslingual prediction of human reading patterns.

Abstract: In 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.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/nu-hlt-at-cmcl-2022-shared-task-multilingual/code)