Transformer architectures have widespread applications, particularly in Natural Language
Processing and Computer Vision. Recently, Transformers have been employed in various …

Transformers are arguably the main workhorse in recent natural language processing
research. By definition, a Transformer is invariant with respect to reordering of the input …

The evaluation campaign of the 19th International Conference on Spoken Language
Translation featured eight shared tasks:(i) Simultaneous speech translation,(ii) Offline …

As RGB-D sensors become more affordable, using RGB-D images to obtain high-accuracy
6D pose estimation results becomes a better option. State-of-the-art approaches typically …

The stylistic properties of text have intrigued computational linguistics researchers in recent
years. Specifically, researchers have investigated the text style transfer task (TST), which …

While large language models (LLMs) have exhibited impressive instruction-following
capabilities, it is still unclear whether and to what extent they can respond to explicit …

In this paper, we aim to improve abstractive dialogue summarization quality and, at the same
time, enable granularity control. Our model has two primary components and stages: 1) a …

Previous length-controllable summarization models mostly control lengths at the decoding
stage, whereas the encoding or the selection of information from the source document is not …

Position representation is crucial for building position-aware representations in
Transformers. Existing position representations suffer from a lack of generalization to test …

Creating a summarized version of a text document that still conveys precise meaning is an
incredibly complex endeavor in natural language processing (NLP). Abstract text …