Abstract-This paper addresses the challenge of enabling nonexpert users to command robots to perform complex high-level tasks using natural language. It describes an integrated system that combines the power of formal methods with the accessibility of natural language, providing correct-by-construction controllers for high-level specifications that can be implemented, and easy-to-understand feedback to the user on those that cannot be achieved. This is among the first works to close this feedback loop, enabling users to interact with the robot in order to identify a succinct cause of failure and obtain the desired controller. The supported language and logical capabilities are illustrated using examples involving a robot assistant in a hospital.

We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1

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Lexical access during speech comprehension comprises numerous computations, including activation, competition, and selection. The spatio-temporal profile of these processes involves neural activity in peri-auditory cortices at least as early as 200ms after stimulation. Their oscillatory dynamics are less well understood, although reports link alpha band de-synchronization with lexical processing. We used magnetoencephalography (MEG) to examine whether these alpha-related oscillations reflect the speed of lexical access, as would be predicted if they index lexical activation. In an auditory semantic priming protocol, monosyllabic nouns were presented while participants performed a lexical decision task. Spatially-localizing beamforming was used to examine spectro-temporal effects in left and right auditory cortex time-locked to target word onset. Alpha and beta de-synchronization (10-20Hz ERD) was attenuated for words following a related prime compared to an unrelated prime beginning about 270ms after stimulus onset. This timing is consistent with how information about word identity unfolds incrementally in speech, quantified in information-theoretic terms. These findings suggest that alpha de-synchronization during auditory word processing is associated with early stages of lexical access.

This paper examines human perceptions of humanoid robot behavior, specifically how perception is affected by variations in head tracking behavior under constant gestural behavior. Subjects were invited to the lab to "play with Nico," an upper-torso humanoid robot. The follow-up survey asked subjects to rate and write about the experience. A coding scheme originally created to gauge human intentionality was applied to written responses to measure the level of intentionality that subjects perceived in the robot. Subjects were presented with one of four variations of head movement: a motionless head, a smooth tracking head, a tracking head with out smoothed movements, and an avoidance behavior, while a pre-scripted wave and beckon sequence was carried out in all cases.Surprisingly, subjects rated the interaction as most enjoyable and Nico as possessing more intentionality when avoidance and unsmooth tracking were used. These data suggest that naïve users of robots may prefer caricatured and exaggerated behaviors to more natural ones. Also, correlations between ratings across modes suggest that simple features of robot behavior reliably evoke equal changes in many perception scales.