2019
ACL
ACL 2019
Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA
Abstract
AbstractWe explore the extent to which neural networks can learn to identify semantically equivalent sentences from a small variable dataset using an end-to-end training. We collect a new noisy non-standardised user-generated Algerian (ALG) dataset and also translate it to Modern Standard Arabic (MSA) which serves as its regularised counterpart. We compare the performance of various models on both datasets and report the best performing configurations. The results show that relatively simple models composed of 2 LSTM layers outperform by far other more sophisticated attention-based architectures, for both ALG and MSA datasets.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— algerian dialect
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Hot Topic Early Bird
— semantic textual similarity
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Metric Learning
Deep Learning > Architectures > Neural Networks
Natural Language Processing > Applications > Natural Language Inference
Artificial Intelligence > Core AI > Natural Language Processing
Deep Learning > Architectures > Recurrent Neural Networks