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Country-level Arabic Dialect Identification using RNNs with and without Linguistic Features
2021
Workshop on Arabic Natural Language Processing
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This work investigates the value of augmenting recurrent neural networks with feature engineering for the Second Nuanced Arabic Dialect Identification (NADI) Subtask 1.2: Country-level DA identification. We compare the performance of a simple word-level LSTM using pretrained embeddings with one enhanced using feature embeddings for engineered linguistic features. Our results show that the addition of explicit features to the LSTM is detrimental to performance. We attribute this performance loss
dblp:conf/wanlp/IssaAAH21
fatcat:txhc6pzqobflhmpado3m3vfac4