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Found 9 papers, 2 papers with code
Asymmetric feature interaction for interpreting model predictions
In this work, we propose an asymmetric feature interaction attribution explanation model that aims to explore asymmetric higher-order feature interactions in the inference of deep neural NLP models.
Learning Ambiguity from Crowd Sequential Annotations
Most crowdsourcing learning methods treat disagreement between annotators as noisy labelings while inter-disagreement among experts is often a good indicator for the ambiguity and uncertainty that is inherent in natural language.
Modeling sequential annotations for sequence labeling with crowds
To accelerate the marginalization of the proposed model, a valid label sequence inference (VLSE) method is proposed to derive the valid ground-truth label sequences from crowd sequential annotations.
Weak Disambiguation for Partial Structured Output Learning
Existing disambiguation strategies for partial structured output learning just cannot generalize well to solve the problem that there are some candidates which can be false positive or similar to the ground-truth label.
Partial sequence labeling with structured Gaussian Processes
Then confidence measure is introduced in the model to address different contributions of candidate labels, which enables the ground-truth label information to be utilized in parameter learning.
Duration modeling with semi-Markov Conditional Random Fields for keyphrase extraction
Existing methods for keyphrase extraction need preprocessing to generate candidate phrase or post-processing to transform keyword into keyphrase.
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization
It is challenging to train a good intent classifier for a task-oriented dialogue system with only a few annotations.
BERT-CNN: a Hierarchical Patent Classifier Based on a Pre-Trained Language Model
The automatic classification is a process of automatically assigning text documents to predefined categories.
Low-dimensional Semantic Space: from Text to Word Embedding
This article focuses on the study of Word Embedding, a feature-learning technique in Natural Language Processing that maps words or phrases to low-dimensional vectors.
