Predicting Native Language from Gaze

Abstract

A fundamental question in language learning concerns the role of a speaker's first language in second language acquisition. We present a novel methodology for studying this question: analysis of eye-movement patterns in second language reading of free-form text. Using this methodology, we demonstrate for the first time that the native language of English learners can be predicted from their gaze fixations when reading English. We provide analysis of classifier uncertainty and learned features, which indicates that differences in English reading are likely to be rooted in linguistic divergences across native languages. The presented framework complements production studies and offers new ground for advancing research on multilingualism. 1

The Effect of Translationese in Machine Translation Test Sets

“…We make use of the lang2vec tool (Lit-tell et al, 2017) using the URIEL Typological Database (Littell et al, 2016) to compute the similarity between pairs of languages. Similar to the approach of Berzak et al (2017), all the 103 available morphosyntactic features in URIEL are obtained; these are derived from the World Atlas of Language Structures (WALS) (Dryer and Haspelmath, 2013), Syntactic Structures of the Worlds Languages (SSWL) (Collins and Kayne, 2009) and Ethnologue (Lewis et al, 2009). Missing feature values are filled with a prediction from a k-nearest neighbors classifier.…”

“…Truncating features with the same value for all the languages present in our study, 87 features remain, consisting of 60 syntactic features and 27 family tree features. We then measure the level of relatedness between two languages using the linguistic similarity (LS) by Berzak et al (2017) (Equation 1), i.e. the cosine similarity between the URIEL feature vectors for two languages v y and v y .…”

Assessing Language Proficiency from Eye Movements in Reading

“…This feature set was previously used in Berzak et al (2017) and consists of reading times for words within fixed contexts. We extract FP and TF durations for the 900 words in the Fixed Text sentences, resulting in a total of 1,800 WFC features.…”

“…Reading Speed Normalization To reduce bias towards fast readers, the feature representations used for Eyescore are normalized to be invariant to the reading speed of the participant. Specifically, for the S-Clusters and WFC feature sets we follow the normalization procedure of Berzak et al (2017), where for a given participant, the reading time of a word w i according to a fixation metric M is normalized by S M,C , the metric's fixation time per word in the linguistic context C:…”

“…In Rello and Ballesteros (2015) eye movements were used to distinguish between readers with and without dyslexia. Berzak et al (2017) collected the dataset used in our work and used it to predict the first language of non-native English readers from gaze. We build on these studies to motivate our task and design feature representations which encode linguistic factors known to affect the human reading process.…”