Abstract Conversational assistants (CAs) and Task‐oriented ones, in particular, are
designed to interact with users in a natural language manner, assisting them in completing …

Interest in dialog systems has grown substantially in the past decade. By extension, so too
has interest in developing and improving intent classification and slot-filling models, which …

In this paper, I argue that AI-powered voice assistants, just as all technologies, actively
mediate our interpretative structures, including values. I show this by explaining the …

In this article, we present the results of our experiments on sentiment and emotion
recognition for English and Polish texts, aiming to work in the context of a therapeutic …

Training a high performance end-to-end speech (E2E) processing model requires an
enormous amount of labeled speech data, especially in the era of data-centric artificial …

The lack of publicly available evaluation data for low-resource languages limits progress in
Spoken Language Understanding (SLU). As key tasks like intent classification and slot filling …

In this article, we present a machine translation model adapted to the domain of intelligent
virtual assistants (IVA) that can be used to translate training and evaluation resources. Our …

In practical dialogue systems, it is crucial to avoid undesired responses and poor user
experiences by detecting Out-Of-Scope (OOS) intents from user utterances. Currently, to …

This paper describes a challenge on Conversational AI correctness with the goal to develop
Natural Language Understanding models that are robust against speech recognition errors …

In this article, we use probing to investigate phenomena that occur during fine-tuning and
knowledge distillation of a BERT-based natural language understanding (NLU) model. Our …