Autism and the web: using web-searching tasks to detect autism and improve web accessibility

This is an accepted manuscript of an article published by ACM in ACM SIGACCESS Accessibility and Computing on 02/08/2018, available online: https://doi.org/10.1145/3264631.3264633 The accepted version of the publication may differ from the final published version.People with autism consistently exhibit different attention-shifting patterns compared to neurotypical people. Research has shown that these differences can be successfully captured using eye tracking. In this paper, we summarise our recent research on using gaze data from web-related tasks to address two problems: improving web accessibility for people with autism and detecting autism automatically. We first examine the way a group of participants with autism and a control group process the visual information from web pages and provide empirical evidence of different visual searching strategies. We then use these differences in visual attention, to train a machine learning classifier which can successfully use the gaze data to distinguish between the two groups with an accuracy of 0.75. At the end of this paper we review the way forward to improving web accessibility and automatic autism detection, as well as the practical implications and alternatives for using eye tracking in these research areas.Published versio

Assessing text and web accessibility for people with autism spectrum disorder

A thesis submitted in partial ful lment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyPeople with Autism Spectrum Disorder experience di culties with reading comprehension and information processing, which a ect their school performance, employability and social inclusion. The main goal of this work is to investigate new ways to evaluate and improve text and web accessibility for adults with autism. The rst stage of this research involved using eye-tracking technology and comprehension testing to collect data from a group of participants with autism and a control group of participants without autism. This series of studies resulted in the development of the ASD corpus, which is the rst multimodal corpus of text and gaze data obtained from participants with and without autism. We modelled text complexity and sentence complexity using sets of features matched to the reading di culties people with autism experience. For document-level classi cation we trained a readability classi er on a generic corpus with known readability levels (easy, medium and di cult) and then used the ASD corpus to evaluate with unseen user-assessed data. For sentencelevel classi cation, we used for the rst time gaze data and comprehension testing to de ne a gold standard of easy and di cult sentences, which we then used as training and evaluation sets for sentence-level classi cation. The ii results showed that both classi ers outperformed other measures of complexity and were more accurate predictors of the comprehension of people with autism. We conducted a series of experiments evaluating easy-to-read documents for people with cognitive disabilities. Easy-to-read documents are written in an accessible way, following speci c writing guidelines and containing both text and images. We focused mainly on the image component of these documents, a topic which has been signi cantly under-studied compared to the text component; we were also motivated by the fact that people with autism are very strong visual thinkers and that therefore image insertion could be a way to use their strengths in visual thinking to compensate for their di culties in reading. We investigated the e ects images in text have on attention, comprehension, memorisation and user preferences in people with autism (all of these phenomena were investigated both objectively and subjectively). The results of these experiments were synthesised in a set of guidelines for improving text accessibility for people with autism. Finally, we evaluated the accessibility of web pages with di erent levels of visual complexity. We provide evidence of existing barriers to nding relevant information on web pages that people with autism face and we explore their subjective experiences with searching the web through survey questions

Predicting reading difficulty for readers with autism spectrum disorder

People with autism experience various reading comprehension difficulties, which is one explanation for the early school dropout, reduced academic achievement and lower levels of employment in this population. To overcome this issue, content developers who want to make their textbooks, websites or social media accessible to people with autism (and thus for every other user) but who are not necessarily experts in autism, can benefit from tools which are easy to use, which can assess the accessibility of their content, and which are sensitive to the difficulties that autistic people might have when processing texts/websites. In this paper we present a preliminary machine learning readability model for English developed specifically for the needs of adults with autism. We evaluate the model on the ASD corpus, which has been developed specifically for this task and is, so far, the only corpus for which readability for people with autism has been evaluated. The results show that out model outperforms the baseline, which is the widely-used Flesch-Kincaid Grade Level formula.paper presented at LREC 2016 Workshop ”Improving Social Inclusion Using NLP: Tools and Resources” held on 23 May 2016 – Portorož, Slovenia

Independent or chain-affiliated hotel? The dilemma of hotel employees

Purpose – This study examines the relationships between hospitality work experience factors and employees’ preference to work in a chain or independent hotel. Methodology/Design/Approach – Quantitative data were collected from hotel employees in Bulgaria who worked in independent and chain hotels. A total of 150 valid responses were used to conduct factor and regression analyses. Findings – The results illustrate that chain hotels provide a better set of operational standards and guidelines, more and better training than independent ones. They also give more opportunities to their employees for career development, better job security and work experience, but competition among employees in chain hotels is higher than in independent properties, and their employees’ salaries are not always more competitive. The factor analysis showed the existence of five factors. Additionally, ‘Communication and decision-making’ and ‘Resources and planning’ were more important than ‘Remuneration and working conditions’ and ‘Training and development’ in shaping employees’ preferences. However, ‘Workload and stress’ was not an important driver of respondents’ choice. Finally, demographic variables had no role in shaping respondents’ preferences. Originality of the research – This paper is one of the first to examine the factors that influence hotel employees’ preferences for working in chains or independent hotels

Predicting the difficulty of multiple choice questions in a high-stakes medical exam

Predicting the construct-relevant difficulty of Multiple-Choice Questions (MCQs) has the potential to reduce cost while maintaining the quality of high-stakes exams. In this paper, we propose a method for estimating the difficulty of MCQs from a high-stakes medical exam, where all questions were deliberately written to a common reading level. To accomplish this, we extract a large number of linguistic features and embedding types, as well as features quantifying the difficulty of the items for an automatic question-answering system. The results show that the proposed approach outperforms various baselines with a statistically significant difference. Best results were achieved when using the full feature set, where embeddings had the highest predictive power, followed by linguistic features. An ablation study of the various types of linguistic features suggested that information from all levels of linguistic processing contributes to predicting item difficulty, with features related to semantic ambiguity and the psycholinguistic properties of words having a slightly higher importance. Owing to its generic nature, the presented approach has the potential to generalize over other exams containing MCQs

Combining Multiple Corpora for Readability Assessment for People with Cognitive Disabilities

The 12th Workshop on Innovative Use of NLP for Building Educational Applications, 8th September 2017 Copenhagen, Denmark.Given the lack of large user-evaluated corpora in disability-related NLP research (e.g. text simplification or readability assessment for people with cognitive disabilities), the question of choosing suitable training data for NLP models is not straightforward. The use of large generic corpora may be problematic because such data may not reflect the needs of the target population. At the same time, the available user-evaluated corpora are not large enough to be used as training data. In this paper we explore a third approach, in which a large generic corpus is combined with a smaller population-specific corpus to train a classifier which is evaluated using two sets of unseen user-evaluated data. One of these sets, the ASD Comprehension corpus, is developed for the purposes of this study and made freely available. We explore the effects of the size and type of the training data used on the performance of the classifiers, and the effects of the type of the unseen test datasets on the classification performance

Independent or chain-affiliated hotel? the dilemma of hotel employees

Purpose – This study examines the relationships between hospitality work experience factors and employees’ preference to work in a chain or independent hotel. Methodology/Design/Approach – Quantitative data were collected from hotel employees in Bulgaria who worked in independent and chain hotels. A total of 150 valid responses were used to conduct factor and regression analyses. Findings – The results illustrate that chain hotels provide a better set of operational standards and guidelines, more and better training than independent ones. They also give more opportunities to their employees for career development, better job security and work experience, but competition among employees in chain hotels is higher than in independent properties, and their employees’ salaries are not always more competitive. The factor analysis showed the existence of five factors. Additionally, ‘Communication and decision-making’ and ‘Resources and planning’ were more important than ‘Remuneration and working conditions’ and ‘Training and development’ in shaping employees’ preferences. However, ‘Workload and stress’ was not an important driver of respondents’ choice. Finally, demographic variables had no role in shaping respondents’ preferences. Originality of the research – This paper is one of the first to examine the factors that influence hotel employees’ preferences for working in chains or independent hotels

Effects of lexical properties on viewing time per word in autistic and neurotypical readers

Eye tracking studies from the past few decades have shaped the way we think of word complexity and cognitive load: words that are long, rare and ambiguous are more difficult to read. However, online processing techniques have been scarcely applied to investigating the reading difficulties of people with autism and what vocabulary is challenging for them. We present parallel gaze data obtained from adult readers with autism and a control group of neurotypical readers and show that the former required higher cognitive effort to comprehend the texts as evidenced by three gaze-based measures. We divide all words into four classes based on their viewing times for both groups and investigate the relationship between longer viewing times and word length, word frequency, and four cognitively-based measures (word concreteness, familiarity, age of acquisition and imagability).University of Wolverhampton and German Research Foundation (DFG

Using gaze data to predict multiword expressions

In recent years gaze data has been increasingly used to improve and evaluate NLP models due to the fact that it carries information about the cognitive processing of linguistic phenomena. In this paper we conduct a preliminary study towards the automatic identification of multiword expressions based on gaze features from native and non-native speakers of English. We report comparisons between a part-ofspeech (POS) and frequency baseline to: i) a prediction model based solely on gaze data and ii) a combined model of gaze data, POS and frequency. In spite of the challenging nature of the task, best performance was achieved by the latter. Furthermore, we explore how the type of gaze data (from native versus non-native speakers) affects the prediction, showing that data from the two groups is discriminative to an equal degree. Finally, we show that late processing measures are more predictive than early ones, which is in line with previous research on idioms and other formulaic structures.Na

Cognitive processing of multiword expressions in native and non-native speakers of English: evidence from gaze data

Gaze data has been used to investigate the cognitive processing of certain types of formulaic language such as idioms and binominal phrases, however, very little is known about the online cognitive processing of multiword expressions. In this paper we use gaze features to compare the processing of verb - particle and verb - noun multiword expressions to control phrases of the same part-of-speech pattern. We also compare the gaze data for certain components of these expressions and the control phrases in order to find out whether these components are processed differently from the whole units. We provide results for both native and non-native speakers of English and we analyse the importance of the various gaze features for the purpose of this study. We discuss our findings in light of the E-Z model of reading