Textual language is the most natural carrier of human emotion. In natural language
processing, textual emotion recognition (TER) has become an important topic due to its …

Text classification as a fundamental task in Natural Language Processing (NLP). Graph
neural networks can better handle the large amount of information in text, and effective and …

Transformers have emerged as a powerful tool for a broad range of natural language
processing tasks. A key component that drives the impressive performance of Transformers …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

Soft sensors have been increasingly applied for quality prediction in complex industrial
processes, which often have different scales of topology and highly coupled spatiotemporal …

Deep neural networks (DNNs) are often used for text classification due to their high
accuracy. However, DNNs can be computationally intensive, requiring millions of …

Pre-trained language models have proven their unique powers in capturing implicit
language features. However, most pre-training approaches focus on the word-level training …

Sarcasm identification on text documents is one of the most challenging tasks in natural
language processing (NLP), has become an essential research direction, due to its …

We propose a novel hierarchical graph-based text classification framework that leverages
the power of contextual node embedding and BERT-based dynamic fusion to capture the …

We introduce a method called TracIn that computes the influence of a training example on a
prediction made by the model. The idea is to trace how the loss on the test point changes …