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Found 13 papers, 5 papers with code
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition
This imbalance leads to misclassifications of the entity classes as the O-class.
Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives
With the dramatic advances in deep learning technology, machine learning research is focusing on improving the interpretability of model predictions as well as prediction performance in both basic and applied research.
Feedback is Needed for Retakes: An Explainable Poor Image Notification Framework for the Visually Impaired
We propose a simple yet effective image captioning framework that can determine the quality of an image and notify the user of the reasons for any flaws in the image.
DM$^2$S$^2$: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention
There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce.
Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents
The proposed framework consists of a bidirectional encoder representations from transformers (BERT)-based detector that detects sentences causing differences in emotion recognition and an analysis that acquires expressions that characteristically appear in such sentences.
Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
To improve the prediction performance for the two different types of discontinuations and for the ad creatives that contribute to sales, we introduce two new techniques: (1) a two-term estimation technique with multi-task learning and (2) a click-through rate-weighting technique for the loss function.
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training
That is, even if the model using our VAT-based technique is trained on unlabeled data from a source other than the target task, both the prediction performance and model interpretability can be improved.
Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation
We propose a new character-based text classification framework for non-alphabetic languages, such as Chinese and Japanese.
Attention Meets Perturbations: Robust and Interpretable Attention with Adversarial Training
To overcome the vulnerability to perturbations in the mechanism, we are inspired by adversarial training (AT), which is a powerful regularization technique for enhancing the robustness of the models.
AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced Loss
Classical and some deep learning techniques for Arabic text classification often depend on complex morphological analysis, word segmentation, and hand-crafted feature engineering.
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative
Furthermore, conditional attention focuses attention of each ad creative with the consideration of its genre and target gender, thus improving conversion prediction accuracy.
End-to-End Text Classification via Image-based Embedding using Character-level Networks
For analysing and/or understanding languages having no word boundaries based on morphological analysis such as Japanese, Chinese, and Thai, it is desirable to perform appropriate word segmentation before word embeddings.
Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning
In this report, we introduce the outline of our system in Task 3: Disease Classification of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection.
