Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

The whole sentence representation reasoning process simultaneously comprises a
sentence representation module and a semantic reasoning module. This paper combines …

As the vital technology of natural language understanding, sentence representation
reasoning technology mainly focuses on sentence representation methods and reasoning …

Document-level relation extraction aims to extract relations among entities within a
document. Different from sentence-level relation extraction, it requires reasoning over …

This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a
dataset designed for use in the development and evaluation of machine learning models for …

This paper proposes a new model for extracting an interpretable sentence embedding by
introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the …

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 …

Reasoning and inference are central to human and artificial intelligence. Modeling inference
in human language is very challenging. With the availability of large annotated data …

Natural language sentence matching is a fundamental technology for a variety of tasks.
Previous approaches either match sentences from a single direction or only apply single …