# Error Detection for Arabic Text Using Neural Sequence Labeling
## 7 Conclusions, Limitations and Future Work  
In this paper we focused on the detection of errors in MSA text using neural sequence labeling. We presented a background and related work on Arabic language and approaches to Automated Grammatical Error Detection and Correction for Arabic and other languages. We also described the corpora we created for this task and presented our experimental setting. Finally, we showed our results and discussed them.  
One of the main limitations of this project was the lack of data, which in turn called for using cross-validation. Although we were satisfied with the results, models were simplified in order to be able to accommodate the small dataset. However, we believe that larger data and larger networks would be able to generalize more when presented with new data. Additionally, our corpora sentences were short (maximum 17 words); however, we believe that the model would have benefited from longer sentences in order to learn from longer sequences of words.  
Furthermore, the fact that neural networks are stochastic required the presentation of fixed seeds into random number generators. It has been shown that results may change when different seed numbers are used. Therefore, every set of hyperparameter configurations needs to be evaluated a number of times for every seed number, such that results are averaged and compared. A number of 5 up to 30 seeds could be used randomly. This process would enable drawing conclusions and making fair comparisons between different models [52]. In our case, we fixed a single seed value for all experiments (to 42), because, for the lack of resources, we could not run every configuration multiple times for different seeds. Nevertheless, this does not hinder the fact that our experiments are reproducible.  
The results shown in this paper can be extended and improved in a number of ways. We suggest using a larger training set with longer sentences, conducting more experimentation with more hyperparameters, using deeper networks with larger embedding size, and running configurations multiple times using a variety of seed values and reporting the averaged results. Additionally, evaluating the performance of the different proposed neural network architectures per error type could be useful and may facilitate a vehicle for comparing systems. Moreover, incorporating the POS tags in the neural network could further improve the error detection.  
As far as we know, we are presenting the first results of applying neural sequence labeling on Arabic text. Additionally, these are the first results of using the A7'ta corpus [33] in an experiment. We believe that the configurations we reported would show good performance on any other comparable Arabic corpus. The code for the experiments is available at [[https://gist.github.com/iwan-rg/4e7522a53e664607c2a3e664f4c076a](https://gist.github.com/iwan-rg/4e7522a53e664607c2a3e664f4c076a)]([https://gist.github.com/iwan-rg/4e7522a53e664607c2a3e664f4c076a](https://gist.github.com/iwan-rg/4e7522a53e664607c2a3e664f4c076a)).  
Conceptualization, N.M. and H.A.-K.; methodology, N.M. and H.A.-K.; software, N.M.; validation, N.M.; formal analysis, N.M. and H.A.-K.; investigation, N.M.; resources, N.M.; data curation, N.M.; writing--original draft preparation, N.M.; writing--review and editing, H.A.-K.; visualization, N.M. and H.A.-K.; supervision, H.A.-K.; project administration, H.A.-K. All authors have read and agreed to the published version of the manuscript.  
Research was funded by the \"Research Center of the Female Scientific and Medical Colleges\", Deanship of Scientific Research, King Saud University.  
The authors declare no conflict of interest.