This study is the first work on a transductive transfer learning approach for low-resource
neural machine translation applied to the Algerian Arabic dialect. The transductive approach …

Prompt-tuning has emerged as an attractive paradigm for deploying large-scale language
models due to its strong downstream task performance and efficient multitask serving ability …

Current approaches for clinical information extraction are inefficient in terms of
computational costs and memory consumption, hindering their application to process large …

Transfer learning is an effective method to improve the performance of low-resource
translation, but its effectiveness heavily relies on specific languages, and transferring …

Hybrid tabular-textual question answering (QA) requires reasoning from heterogeneous
information, and the types of reasoning are mainly divided into numerical reasoning and …

Most of the previous neural machine translations (NMT) rely on parallel corpus. Integrating
explicitly prior syntactic structure information can improve the neural machine translation. In …

Transformers have emerged as a pivotal tool in machine translation. Nonetheless, their
effectiveness typically hinges on extensive training with millions of bilingual parallel corpora …

The good performance of Neural Machine Trans-lation (NMT) normally relies on a large
amount of parallel data, while the bilingual data between languages are usually insufficient …

In India, regional languages play an important role in government-to-public, public-to-citizen
rights, weather forecasting and farming. Depending on the state the language also changes …

Stacking non-linear layers allows deep neural networks to model complicated functions, and
including residual connections in Transformer layers is beneficial for convergence and …