[PDF] Top 20 Neural Network Architectures for Arabic Dialect Identification
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Neural Network Architectures for Arabic Dialect Identification
... end-to-end neural networks taking as input acoustic representations of audio files and as output the dialect ...Language Identification (Ganapathy et ...Recurrent Neural Network (RNN) ... See full document
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Deep Models for Arabic Dialect Identification on Benchmarked Data
... model architectures fare on the dialect identification task, we use classifiers with pre-defined hyper-parameters inspired by previous works as described in Section ... See full document
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Character Level Convolutional Neural Network for German Dialect Identification
... Convolutional Neural Networks (CNN) were invented to deal with images and they have achieved ex- cellent results in computer vision (Krizhevsky et al., 2012; Sermanet et al., 2013; Ji et al., 2013). Later, it has ... See full document
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Arabic Dialect Identification for Travel and Twitter Text
... on Arabic Fine-Grained Di- alect Identification. Dialect Identification is one of the prominent tasks in the field of Natu- ral language processing where the subsequent language modules can be ... See full document
5
Hierarchical Deep Learning for Arabic Dialect Identification
... of 128 neurons followed by a fully-connected layer of size 64 and a fully-connected layer of size 8 with softmax activation for the output. The level two is a set of 7 DNNs. For each of this models the size of the layers ... See full document
5
A Character Level Convolutional BiLSTM for Arabic Dialect Identification
... MSA dialect to the previous three-way ...convolution neural network with a GRU layer for a five-way classification task (MSA, Egyptian, Gulf, Levantine, and North ... See full document
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Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge
... deep neural net- works which consists of multiple interconnected layers between the input and output layers was used in speech recognition tasks(Najafian et ...2016). Neural networks can be configured to ... See full document
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The MADAR Shared Task on Arabic Fine Grained Dialect Identification
... (SVM). Neural refers to any neural network based model such as bidirectional long short-term mem- ory (BiLSTM), or convolutional neural network ... See full document
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LIUM MIRACL Participation in the MADAR Arabic Dialect Identification Shared Task
... 25 Arabic dif- ferent dialects from east to ...corpus. Dialect Identification (DID) is already a hard task, even when taking into account only 5 ... See full document
5
Arabic Dialect Identification with Deep Learning and Hybrid Frequency Based Features
... The model architecture in Figure 2 consists of two hidden fully connected layers followed by an output layer. The two hidden layers are followed by ReLU activations and dropout layers with 20% probability. The number of ... See full document
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Mawdoo3 AI at MADAR Shared Task: Arabic Tweet Dialect Identification
... SepCNN: Stands for Separable Convolutional Neural Networks (Denk, 2017), and is composed of two consecutive convolutional layers. The first is operating on the spatial dimension and per- formed on channels ... See full document
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JHU System Description for the MADAR Arabic Dialect Identification Shared Task
... For the word-based neural models, we use 300- dimensional word embeddings trained on differ- ent amounts of data as input representations. First, we use randomly initialized embeddings. Then, we train fastText ... See full document
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Building a Speech Corpus based on Arabic Podcasts for Language and Dialect Identification
... new Arabic speech corpus made of Arabic audio ...multi-dialectal identification tasks. It includes two lan- guages: Modern Standard Arabic (MSA) and English, and four Arabic dialects: ... See full document
5
The SMarT Classifier for Arabic Fine Grained Dialect Identification
... language identification sys- tem that can distinguish between several Arabic dialects, we tested three methods namely sim- ple neural networks (LSTM) (Sak et ... See full document
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Tübingen system in VarDial 2017 shared task: experiments with language identification and cross lingual parsing
... with neural network models. The performance of neural network models was close but always below the corre- sponding SVM classifiers in the discrim- ination ... See full document
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Character Level Convolutional Neural Network for Arabic Dialect Identification
... convolution neural network, the same approach we are using, but they only used the ASR transcripts of Arabic speech as the acoustic features were not available at that ...of Arabic speech ... See full document
6
UnibucKernel: An Approach for Arabic Dialect Identification Based on Multiple String Kernels
... authorship identification, plagiarism detection or similar text mining ...the Arabic Dialect Identification (ADI) Shared Task of the DSL 2016 Challenge (Malmasi et ...Standard Arabic ... See full document
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Classifying Arabic dialect text in the Social Media Arabic Dialect Corpus (SMADC)
... from our Arabic dialect corpora. The classifica- tion process was based on deleting all MSA words from the document then checking each word in the document by searching the dialect dictionaries. The ... See full document
9
Arabic Dialect Identification in Speech Transcripts
... regional Arabic dialects (Egyptian, Gulf, Levantine, North African) and Modern Standard Arabic (MSA) in a transcribed speech ...the Arabic Dialect Identification sub-task of the 2016 ... See full document
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AIDA2: A Hybrid Approach for Token and Sentence Level Dialect Identification in Arabic
... 80.9%, 79.6%, and 75.1% for the Egyptian-MSA, Levantine-MSA, and Gulf-MSA classification, re- spectively. These results support the common as- sumption that Egyptian, relative to the other Ara- bic dialectal variants, is ... See full document
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