# Error Detection for Arabic Text Using Neural Sequence Labeling
## 1 Introduction  
Error detection may help both natives and non-natives in producing correctly written text. Moreover, error detection has a wide range of applications, such as automatic essay scoring [1], style and grammar suggestion [2, 3], and language learning systems [4]. Additionally, specific types of errors in a text may reflect the writer's age, proficiency, dialect, and native language. Therefore, the output of error detection models could be used for creating a profile of a certain writer [5]. Such profiles can be used for author identification, native language identification, or even the level of education.  
Using neural network models has shown significant results for different Natural Language Processing (NLP) tasks [6]. There exist a variety of neural network architectures, ranging from Recurrent Neural Networks (RNNs) [7] to more advanced Long-Short-Term Memory Networks (LSTMs) [8] and other varieties, such as bidirectional LSTMs (BiLSTMs) [9]. RNN and its variations are very appealing for NLP as they are specialized in learning sequential data including text. Hence, they are commonly used for the sequence classification task and have also recently found success at automating grammatical error detection [1] and correction [10] through language modeling [11] and machine translation [12].  
Although such approaches have been used to detect errors in English text, error detection based on neural networks has not yet been explored for Arabic. Essentially, earlier state-of-the-artsystems for Arabic depend on employing combinations of rule and statistical-based approaches [3]. However, taking into account the number and complexity of Arabic grammar rules, there is a need for more advanced approaches that can cope with these challenges.  
In this paper, we treat error detection as an independent task and present the first experiments using neural network models for the task of error detection in Arabic text. We mainly investigate and report the results of comparing three neural networks architectures: SimpleRNN, LSTM, and BiLSTM. We also explore the effects of training datasets with different sizes on the overall performance by providing additional training data to each one of the models. The details will be presented for the dataset, which we created and later augmented especially for the task of error detection for Arabic. The types of errors included in the dataset are syntax errors, morphological errors, semantic errors, linguistic errors, stylistic errors, spelling errors, and the use of informal as well as borrowed words. Erroneous words that appeared within some sentences in the corpus are presented in Table 1, showing a sample for each type of error and the reason it is considered an error.  
The rest of the paper is organized as follows: in Section 2, we give a background and overview of the related work; the details of our corpus are presented in Section 3; implementation and experiments are described in Section 4; results from our experimentation are illustrated in Section 5; Section 6 provides discussion and analysis of the results is presented; Section 7 concludes the paper along with limitations and future work.