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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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Towards Understanding Egyptian Arabic Dialogues ...
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Abstract:
Labelling of user's utterances to understanding his attends which called Dialogue Act (DA) classification, it is considered the key player for dialogue language understanding layer in automatic dialogue systems. In this paper, we proposed a novel approach to user's utterances labeling for Egyptian spontaneous dialogues and Instant Messages using Machine Learning (ML) approach without relying on any special lexicons, cues, or rules. Due to the lack of Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus includes 4725 utterances for three domains, which are collected and annotated manually from Egyptian call-centers. The system achieves F1 scores of 70. 36% overall domains. ... : arXiv admin note: substantial text overlap with arXiv:1505.03081 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1509.03208 https://arxiv.org/abs/1509.03208
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