Abstract
Individuals with autism spectrum disorder (ASD) experience difficulties in social aspects of communication, but the linguistic characteristics associated with deficits in discourse and pragmatic expression are often difficult to precisely identify and quantify. We are currently collecting a corpus of transcribed natural conversations produced in an experimental setting in which participants with and without ASD complete a number of collaborative tasks with their neurotypical peers. Using this dyadic conversational data, we investigate three pragmatic features - politeness, uncertainty, and informativeness - and present a dataset of utterances annotated for each of these features on a three-point scale. We then introduce ongoing work in developing and training neural models to automatically predict these features, with the goal of identifying the same between-groups differences that are observed using manual annotations. We find the best performing model for all three features is a feedforward neural network trained with BERT embeddings. Our models yield higher accuracy than ones used in previous approaches for deriving these features, with F1 exceeding 0.82 for all three pragmatic features.
Highlights
Autism spectrum disorder (ASD) is a neurological disorder associated with impairments in communication that can have a life-long impact on relationships, professional success, and personal independence (Ketelaars et al, 2010; Whitehouse et al, 2009; Hendricks, 2010)
Work on computational approaches for automatically identifying these features in the expressive language of individuals with ASD has focused exclusively on the language of children. This prior research has generally been applied to expressive language produced in a semi-structured context with an examiner or parent rather than spontaneous conversational speech with a peer (Prud’hommeaux et al, 2014; Losh and Gordon, 2014; Parish-Morris et al, 2016; Goodkind et al, 2018). Our work addresses these aforementioned shortcomings in the previous work on pragmatic expression in ASD
We propose several neural models for classifying utterances according to these features, and we explore whether our automated methods of generating these pragmatic features can be used to distinguish adults with ASD from their neurotypical peers as effectively as features derived via manual annotation
Summary
Autism spectrum disorder (ASD) is a neurological disorder associated with impairments in communication that can have a life-long impact on relationships, professional success, and personal independence (Ketelaars et al, 2010; Whitehouse et al, 2009; Hendricks, 2010). Some percentage of individuals with ASD are not verbal from a young age, most go on to acquire spoken language but experience challenges in social aspects of communication related to discourse and pragmatic expression (Eales, 1993; Young et al, 2005). This atypicality in language has been recognized since the disorder was first named nearly eighty years ago (Kanner, 1943), and unusual language usage is one of the criteria used in the primary diagnostic instruments for ASD (Lord et al, 2002; Rutter et al, 2003). This prior research has generally been applied to expressive language produced in a semi-structured context with an examiner or parent rather than spontaneous conversational speech with a peer (Prud’hommeaux et al, 2014; Losh and Gordon, 2014; Parish-Morris et al, 2016; Goodkind et al, 2018)
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