Continual learning (CL) aims to enable information systems to learn from a continuous data
stream across time. However, it is difficult for existing deep learning architectures to learn a …

Deep learning approaches have achieved great success in the field of Natural Language
Processing (NLP). However, directly training deep neural models often suffer from overfitting …

In this work, we show that it is feasible to perform multiple tasks concurrently on point cloud
with a straightforward yet effective multi-task network. Our framework, Poly-PC, tackles the …

Recent studies have demonstrated promising outcomes by employing large language
models with multi-tasking capabilities. They utilize prompts to guide the model's behavior …

We investigate the problem of manually correcting errors from an automatic speech
transcript in a cost-sensitive fashion. This is done by specifying a fixed time budget, and then …

The usefulness of translation quality estimation (QE) to increase productivity in a computer-
assisted translation (CAT) framework is a widely held assumption (Specia, 2011; Huang et …

We investigate different strategies for combining quality estimation (QE) and automatic
postediting (APE) to improve the output of machine translation (MT) systems. The joint …

This paper describes the system by FBK HLT-MT for cross-lingual semantic textual similar-ity
measurement. Our approach is based on supervised regression with an ensemble deci-sion …

Multitask learning has been proven a useful technique in a number of Natural Language
Processing applications where data is scarce and naturally diverse. Examples include …

Most current quality estimation (QE) models for machine translation are trained and
evaluated in a static setting where training and test data are assumed to be from a fixed …