Research at the Interaction Lab focuses on human-agent communication using
conversational Natural Language. The ultimate goal is to create systems where humans and …

While Transformer-based text classifiers pre-trained on large volumes of text have yielded
significant improvements on a wide range of computational linguistics tasks, their …

We present a new neural architecture for wide-coverage Natural Language Understanding
in Spoken Dialogue Systems. We develop a hierarchical multi-task architecture, which …

Disfluencies (ie interruptions in the regular flow of speech), are ubiquitous to spoken
discourse. Fillers (" uh"," um") are disfluencies that occur the most frequently compared to …

Abstract Automatic Speech Recognition (ASR) systems are increasingly powerful and more
accurate, but also more numerous with several options existing currently as a service (eg …

In modern interactive speech-based systems, speech is consumed and transcribed
incrementally prior to having disfluencies removed. This post-processing step is crucial for …

Dialog acts can be interpreted as the atomic units of a conversation, more fine-grained than
utterances, characterized by a specific communicative function. The ability to structure a …

We present a multi-task learning framework to enable the training of one universal
incremental dialogue processing model with four tasks of disfluency detection, language …

As voice user interfaces and conversational agents grow in importance, automatic speech
recognition (ASR) encounters increasingly free-form and informal input data. Conversational …

People rarely speak in the same manner that they write–they are generally disfluent.
Disfluencies can be defined as interruptions in the regular flow of speech, such as pausing …