Comparing the Performance of CNNs and Shallow Models for Language Identification
In this work we compare the performance of convolutional neural networks and shallow models on three out of the four language identification shared tasks proposed in the VarDial Evaluation Campaign 2021. In our experiments, convolutional neural networks and shallow models yielded comparable performance in the Romanian Dialect Identification (RDI) and the Dravidian Language Identification (DLI) shared tasks, after the training data was augmented, while an ensemble of support vector machines and Naïve Bayes models was the best performing model in the Uralic Language Identification (ULI) task. While the deep learning models did not achieve state-of-the-art performance at the tasks and tended to overfit the data, the ensemble method was one of two methods that beat the existing baseline for the first track of the ULI shared task.
Code (1)
Tasks
Dialect IdentificationLanguage IdentificationSimilar Papers 제목 키워드 기반
KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution
This paper presents the first application of Kolmogorov-Arnold Convolution for Text (KAConvText) in sentence classification, addressing three tasks: imbalanced binary hate speech detection, balanced multiclass news class…
Sentence ClassificationLanguage IdentificationHate Speech DetectionNews ClassificationConvolution with Logarithmic Filter Groups for Efficient Shallow CNN
In convolutional neural networks (CNNs), the filter grouping in convolution layers is known to be useful to reduce the network parameter size. In this paper, we propose a new logarithmic filter grouping which can capture…
ClassificationFacial Expression RecognitionFacial Expression Recognition (FER)General ClassificationModeling biological face recognition with deep convolutional neural networks
Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground and recent efforts ha…
Face DetectionFace IdentificationFace RecognitionObject RecognitionHierarchical Transfer Convolutional Neural Networks for Image Classification
In this paper, we address the issue of how to enhance the generalization performance of convolutional neural networks (CNN) in the early learning stage for image classification. This is motivated by real-time application…
ClassificationGeneral Classificationimage-classificationImage ClassificationLearning Spatio-Temporal Features with Two-Stream Deep 3D CNNs for Lipreading
We focus on the word-level visual lipreading, which requires recognizing the word being spoken, given only the video but not the audio. State-of-the-art methods explore the use of end-to-end neural networks, including a …
General ClassificationLipreadingOptical Flow Estimation