Sentence semantic equivalence identification (SSEI) targets to measure the semantic
equivalence between two sentences. To supplement limited supervision, existing methods …

With the development of Internet of Things and cloud computing, intelligent question-
answering (QA) has brought great convenience to human's daily activities. As one of the …

With the development of pre-trained models (PTMs), the performance of text classification
has been continuously improved by directly employing the features generated by PTMs …

With the growth of the consumer electronics market, the software development industry is
facing new opportunities and an increased focus on code retrieval techniques to improve …

The success of many web services relies on the large-scale domain-specific high-quality
labeled dataset. Insufficient public datasets motivate us to reduce the cost of data labeling …

In the task of matching Chinese sentences, the key semantics within sentences and the
deep interaction between them significantly affect the matching performance. However …

Text generation is crucial for many applications in natural language processing. With the
prevalence of deep learning, the encoder-decoder architecture is dominantly adopted for …

The semantic matching of questions is a fundamental aspect of retrieval-based question
answering (QA) systems. Text representations containing rich semantic information are …

Most of the current deep learning based sentence semantic matching uses Siamese network
to extract semantic features and then uses simple attention mechanism for interaction, which …

Sentence semantic matching (SSM) is central to many natural language processing tasks.
This is especially the case for Chinese sentence semantic matching due to the complexity of …