AmericasNLI: Machine translation and natural language inference systems for Indigenous languages of the Americas.

Little attention has been paid to the development of human language technology for truly low-resource languages-i.e., languages with limited amounts of digitally available text data, such as Indigenous languages.

Removing noise from event-related potentials using a probabilistic generative model with grouped covariance matrices.

Analysis of electroencephalograms (EEG) usually suffers from a variety of noises. In this paper, we propose a new method for background noise removal from single-trial event-related potentials (ERPs) recorded with a multi-channel EEG. An observed signal is separated into multiple signals with a multi-channel Wiener filter, whose coefficients are estimated based on a probabilistic generative model in the time-frequency domain.

Automated social skills training with audiovisual information.

People with social communication difficulties tend to have superior skills using computers, and as a result computer-based social skills training systems are flourishing. Social skills training, performed by human trainers, is a well-established method to obtain appropriate skills in social interaction. Previous works have attempted to automate one or several parts of social skills training through human-computer interaction.

An evaluation of EEG ocular artifact removal with a multi-channel wiener filter based on probabilistic generative model.

Data contamination by ocular artifacts such as eye blinks and eye movements is a major barrier that must be overcome when attempting to analyze electroencephalogram (EEG) and event-related potential (ERP) data. To handle this problem, a number of artifact removal methods has been proposed. Specifically, we focus on a method using a multi-channel Wiener filters based on a probabilistic generative model.