Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries,
which is a core component in a task-oriented dialog system. With the burst of deep neural …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

The remarkable success of transformers in the field of natural language processing has
sparked the interest of the speech-processing community, leading to an exploration of their …

General-purpose language models have demonstrated impressive capabilities, performing
on par with state-of-the-art approaches on a range of downstream natural language …

This paper presents the results of the news translation task, the multilingual low-resource
translation for Indo-European languages, the triangular translation task, and the automatic …

Cross-domain named entity recognition (NER) models are able to cope with the scarcity
issue of NER samples in target domains. However, most of the existing NER benchmarks …

During a public health crisis like the COVID-19 pandemic, a credible and easy-to-access
information portal is highly desirable. It helps with disease prevention, public health …

Scaling semantic parsing models for task-oriented dialog systems to new languages is often
expensive and time-consuming due to the lack of available datasets. Available datasets …

In recent years, fostered by deep learning technologies and by the high demand for
conversational AI, various approaches have been proposed that address the capacity to …

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented
dialogue in unseen domains without the expense of collecting in-domain data. In this paper …