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Found 8 papers, 4 papers with code
Rank-Aware Negative Training for Semi-Supervised Text Classification
To alleviate the noisy information, we adapt a reasoning with uncertainty-based approach to rank the unlabeled texts based on the evidential support received from the labeled texts.
ZLPR: A Novel Loss for Multi-label Classification
To support the application of deep learning in multi-label classification (MLC) tasks, we propose the ZLPR (zero-bounded log-sum-exp \& pairwise rank-based) loss in this paper.
Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition
The ultimate goal is to enable a global view that considers the beginning and the end positions to predict the entity.
BERT-ASC: Implicit Aspect Representation Learning through Auxiliary-Sentence Construction for Sentiment Analysis
Unfortunately, the aspect is often expressed implicitly through a set of representatives and thus renders implicit mapping process unattainable unless sufficient labeled examples are available.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Sparse-softmax: A Simpler and Faster Alternative Softmax Transformation
The softmax function is widely used in artificial neural networks for the multiclass classification problems, where the softmax transformation enforces the output to be positive and sum to one, and the corresponding loss function allows to use maximum likelihood principle to optimize the model.
BioCopy: A Plug-And-Play Span Copy Mechanism in Seq2Seq Models
Copy mechanisms explicitly obtain unchanged tokens from the source (input) sequence to generate the target (output) sequence under the neural seq2seq framework.
Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention
Emotion recognition from speech is a challenging task.
RoFormer: Enhanced Transformer with Rotary Position Embedding
Then, we propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information.
Ranked #1 on
Semantic Text Matching
on CAIL2019-SCM - test
