William Jarrold

Brief Report: Joint Attention and Information Processing in Children with Higher Functioning Autism Spectrum Disorders.

Theory suggests that information processing during joint attention may be atypical in children with Autism Spectrum Disorder (ASD). This hypothesis was tested in a study of school-aged children with higher functioning ASD and groups of children with symptoms of ADHD or typical development. The results indicated that the control groups displayed significantly better recognition memory for pictures studied in an initiating joint attention (IJA) rather than responding to joint attention (RJA) condition. This effect was not evident in the ASD group. The ASD group also recognized fewer pictures from the IJA condition than controls, but not the RJA condition. Atypical information processing may be a marker of the continued effects of joint attention disturbance in school aged children with ASD.

An End-to-End Conversational Second Screen Application for TV Program Discovery

In this article, we report on a multiphase R&D effort to develop a conversational second screen application for TV program discovery. Our goal is to share with the community the breadth of artificial intelligence (AI) and natural language (NL) technologies required to develop such an application along with learnings from target end-users. We first give an overview of our application from the perspective of the end-user. We then present the architecture of our application along with the main AI and NL components, which were developed over multiple phases. The first phase focuses on enabling core functionality such as effectively finding programs matching the user’s intent. The second phase focuses on enabling dialog with the user. Finally, we present two user studies, corresponding to these two phases. The results from both studies demonstrate the effectiveness of our application in the target domain.

An end-to-end dialog system for TV program discovery

In this paper, we present an end-to-end dialog system for TV program discovery that uniquely combines several technologies such as trainable relation extraction, belief tracking over relational structures, mixed-initiative dialog management, and inference over large-scale knowledge graphs. We present an evaluation of our end-to-end system with real users, and found that our system performed well along several dimensions such as usability and task success rate. These results demonstrate the effectiveness of our system in the target domain.

The Social-Emotional Turing Challenge

Social-emotional intelligence is an essential part of being a competent human and is thus required for human-level AI. When considering alternatives to the Turing Test it is therefore a capacity that is important to test. We characterize this capacity as affective theory of mind and describe some unique challenges associated with its interpretive or generative nature. Mindful of these challenges we describe a five-step method along with preliminary investigations into its application. We also describe certain characteristics of the approach such as its incremental nature, and countermeasures that make it difficult to game or cheat.

A TV Program Discovery Dialog System using recommendations

We present an end-to-end conversational system for TV program discovery that uniquely combines advanced technologies for NLU, Dialog Management, Knowledge Graph Inference and Personalized Recommendations. It uses a semantically rich relational representation of dialog state and knowedge graph inference for queries. The recommender combines evidence for user preferences from multiple modalities such as dialog, user viewing history and activity logs. It is tightly integrated with the Dialog System, especially for explanations of recommendations. A demo of the system on a iPad will be shown.

Nuance Reasoning Framework: A Rule-Based System for Semantic Query Rewriting

We present the Nuance Reasoning Framework (NRF), a rule-based framework for semantic query rewriting and reasoning that is being utilized by Nuance Communications Inc. in speech-enabled conversational virtual assistant solutions for numerous automotive Original Equipment Manufacturer’s (OEM). We focus on the semantic rewriting task performed by NRF, which bridges the conceptual mismatch between the natural language front-end of automotive virtual assistants and their back end databases, and personalizes the results to the driver. We also describe many of its powerful features such as rewriter arbitration, query mediation and more.