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Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation ...
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NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task ...
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ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic ...
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AraT5: Text-to-Text Transformers for Arabic Language Generation ...
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DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings ...
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Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19 ...
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Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments ...
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DiaNet: BERT and Hierarchical Attention Multi-Task Learning of Fine-Grained Dialect ...
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Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level ...
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JANA: A Human-Human Dialogues Corpus for Egyptian Dialect ...
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Turn Segmentation into Utterances for Arabic Spontaneous Dialogues and Instance Messages ...
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Abstract:
Text segmentation task is an essential processing task for many of Natural Language Processing (NLP) such as text summarization, text translation, dialogue language understanding, among others. Turns segmentation considered the key player in dialogue understanding task for building automatic Human-Computer systems. In this paper, we introduce a novel approach to turn segmentation into utterances for Egyptian spontaneous dialogues and Instance Messages (IM) using Machine Learning (ML) approach as a part of automatic understanding Egyptian spontaneous dialogues and IM task. Due to the lack of Egyptian dialect dialogue corpus the system evaluated by our corpus includes 3001 turns, which are collected, segmented, and annotated manually from Egyptian call-centers. The system achieves F1 scores of 90.74% and accuracy of 95.98%. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1505.03081 https://dx.doi.org/10.48550/arxiv.1505.03081
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