A Corpus of Text Data and Gaze Fixations from Autistic and Non-Autistic Adults

A Corpus of Text Data and Gaze Fixations from Autistic and Non-autistic Adults Victoria Yaneva*, Irina Temnikova**, Ruslan Mitkov* *Research Institute in Information and Language Processing,University of Wolverhampton, **Qatar Computing Research Institute,HBKU, Doha, Qatar, *Wolverhampton,UK, **Doha,Qatar, V.Yaneva@wlv.ac.uk, itemnikova@qf.org.qa, R.Mitkov@wlv.ac.uk Abstract The paper presents a corpus of text data and its corresponding gaze fixations obtained from autistic and non-autistic readers. The data was elicited through reading comprehension testing combined with eye-tracking recording. The corpus consists of 1034 content words tagged with their POS, syntactic role and three gaze-based measures corresponding to the autistic and control participants. The reading skills of the participants were measured through multiple-choice questions and, based on the answers given, they were divided into groups of skillful and less-skillful readers. This division of the groups informs researchers on whether particular fixations were elicited from skillful or less-skillful readers and allows a fair between-group comparison for two levels of reading ability. In addition to describing the process of data collection and corpus development, we present a study on the effect that word length has on reading in autism. The corpus is intended as a resource for investigating the particular linguistic constructions which pose reading difficulties for people with autism and hopefully, as a way to inform future text simplification research intended for this population. Keywords: autism, eye tracking, reading, gaze fixations, corpus 1. Introduction People with Autism Spectrum Disorder (ASD) struggle to comprehend various aspects of spoken and written lan- guage such as long words and sentences, abstract concepts, figurative language, irony and sarcasm, etc. (Happe, 1997; MacKay and Shaw, 2004; O‘Connor and Klein, 2004). Deficits in reading comprehension have been defined as one of the main reasons for the high rate of school dropout in autistic students, later on affecting their social inclusion and employability prospects (Brugha et al., 2012). In order to address these issues a number of language technology tools have been developed, ranging from Picture Exchange Communications Systems (PECS), which allow users to combine pictures with text in order to convey meaning, to mobile applications such as Stories About Me1 or VAST- Autism2 app, which aim to assist key areas of language de- velopment in autistic users. Other software, such as the OpenBook3 tool, performs semi-automatic text simplifica- tion specifically targeted at autistic readers by reducing syn- tactic complexity and disambiguating meaning. The main issue with the language assistance tools men- tioned above is that they need to rely on robust research into the reading difficulties people with autism face and the spe- cific linguistic components which need to be simplified. So far, the only way reading deficits in autism have been inves- tigated is through psychological testing which casts light on the impairment of underlying cognitive mechanisms in- volved in language processing (Frith and Snowling, 1983; Happe, 1997) rather than the complexity of various linguis- tic features. At the same time, a useful and reliable way of detect- ing linguistic constructions which increase text complex- 1https://itunes.apple.com/us/app/stories- aboutme/id531603747?mt=8, last accessed May 2015. 2https://itunes.apple.com/us/app/vast-autism-1- core/id426041133?mt=8, last …

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