Code-mixed language, characterized by the seamless blending of multiple languages,
presents a formidable challenge for natural language understanding systems. In our work …

Natural Language Understanding (NLU) aims to make sense of language by enabling
computers to comprehend text in semantic level, which is a fundamental but challenging …

Natural language understanding (NLU) is one of the most critical components in goal-
oriented dialog systems and enables innovative Big Data applications such as intelligent …

Natural language understanding (NLU) is an important aspect of achieving human–machine
interactions in the automotive application field, consisting of two core subtasks, multiple …

Learning human languages is a difficult task for a computer. However, Deep Learning (DL)
techniques have enhanced performance significantly for almost all-natural language …

Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP
tasks to capture the long-term and local dependencies, respectively. Attention mechanisms …

Attention-based convolutional neural network models are increasingly adopted for language
recognition tasks. In this paper, based on the self-attention mechanism, we solve the study of …

Abstract Natural Language Inference (NLI) is a fundamental task in natural language
understanding. In spite of the importance of existing research on NLI, the problem of how to …

Generalized text representations are the foundation of many natural language
understanding tasks. To fully utilize the different corpus, it is inevitable that models need to …

Natural Language Inference (NLI) is a branch of Natural Language Processing (NLP) whose
main task is to determine the relationship between a sentence pair (two sentences). Early …