Motivation Few-shot learning that can effectively perform named entity recognition in low-
resource scenarios has raised growing attention, but it has not been widely studied yet in the …

Sentiment recognition in social network aims at recognizing the underlying affective states of
user-generated content. The research center of sentiment recognition is moving from pure …

Due to the development of pre-trained language models, automated code generation
techniques have shown great promise in recent years. However, the generated code will not …

This paper outlines our work in collecting training data for and developing a Latin–German
Neural Machine Translation (NMT) system, for translating 16th century letters. While Latin …

Data sparsity is one of the challenges for low-resource language pairs in Neural Machine
Translation (NMT). Previous works have presented different approaches for data …

We present a generic framework for data augmentation via dependency subtree swapping
that is applicable to machine translation. We extract corresponding subtrees from the …

One of the main problems low-resource languages face in NLP can be pictured as a vicious
circle: data is needed to build and test tools, but the available text is scarce and there are not …

Data augmentation methods for neural machine translation are particularly useful when
limited amount of training data is available, which is often the case when dealing with low …

The Bahnar, a minority ethnic group in Vietnam with ancient roots, hold a language of deep
cultural and historical significance. The government is prioritizing the preservation and …

When the amount of parallel sentences available to train a neural machine translation is
scarce, a common practice is to generate new synthetic training samples from them. A …