Time series are usually rarely or sparsely labeled, which limits the performance of deep
learning models. Self-supervised representation learning can reduce the reliance of deep …

With the emergence of deep learning and the improvement of computing ability, artificial
intelligence has also made great progress. In the case of sufficient data, deep learning models …

Deep learning has become a hot research topic in the field of time series analysis and data
mining. Training models often requires balanced and large data sets, but on the one hand, …

Text matching is a computational task that involves comparing and establishing the semantic
relationship between two textual inputs. The prevailing approach in text matching entails …

Text matching is the task of matching two texts and determining the relationship between
them, which has extensive applications in natural language processing tasks such as reading …

Exposure bias and poor translation diversity are two common problems in neural machine
trans-lation (NMT) which are caused by the general of the teacher forcing strategy for training …

“Natural language sentence matching is the task of comparing two sentences and identifying
the relationship between them. It has a wide range of applications in natural language …

Multimodal Named Entity Recognition (MNER) and Multimodal Relation Extraction (MRE)
are fundamental and core tasks in the field of information extraction. Recently, Joint …

As multilingual text increases, the analysis of multilingual data plays a crucial role in statistical
translation models, cross-language information retrieval, the construction of parallel corpus…

Aiming at the increasingly rich multi language information resources and multi-label data in
scientific literature, in order to mining the relevance and correlation in languages, this paper …