# Columns Occurrences Graph to Improve Column Prediction in Deep Learning [PERSON]
## 7 Conclusions  
In the field of NLIDB, bridging the gap between user-intended columns in a NL query and the predicted columns in the SQL query has been an ongoing issue. To resolve this semantic issue, various methods have been proposed, including preprocessing the data before its entrance to the model to reduce the semantic complexity. We have proposed an approach to include column occurrences scores extracted from previously executed user queries. We extended the base SyntaxSQLNet approach to emphasize column prediction accuracy with the help of a column occurrences scores graph. Column occurrences scores, in a previously executed queries database, shows the commonly used columns of that database by users with respect to the other columns, bridging the gap between predicted SQL query and user intended columns for a particular query. Our experiment shows that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Easy** & **Medium** & **Hard** & **Extra Hard** & **All** \\ \hline SyntaxSQLNET & 43.3\% & 22.8\% & 22.3\% & 4.2\% & 25.3\% \\ \hline DAE [14] & 45.2\% & 30.5\% & 25.7\% & 7.9\% & 29.8\% \\ \hline SyntaxSQLNET + BERT & 56.1\% & 31.7\% & 29.5\% & 8.9\% & 33.7\% \\ \hline Adjective-Noun Phrasing & 67.5\% & 48.2\% & 41.7\% & 14.7\% & 45.4\% \\ Knowledge [16] & 84.2\% & 59.9\% & 59.5\% & 18.6\% & 55.1\% \\ \hline Ours + BERT & 97.6\% & 70.2\% & 67.7\% & 24.8\% & 64.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Exact Match Accuracy Comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Select** & **Where** & **Group by** & **Order by** & **Keywords** \\ \hline SyntaxSQLNET & 62.4\% & 34.1\% & 34.9\% & 56.8\% & 85.9\% \\ \hline DAE & 59.8\% & 40.1\% & 38.4\% & 57.6\% & 91.2\% \\ \hline SyntaxSQLNET + BERT & 71.0\% & 47.7\% & 38.5\% & 61.2\% & 89.3\% \\ \hline Adjective-Noun Phrasing & 70.1\% & 44.8\% & 63.2\% & 67.8\% & 76.8\% \\ Knowledge & 71.5\% & 50.2\% & 65.0\% & 68.5\% & 77.9\% \\ \hline Ours + BERT & 80.2\% & 61.9\% & 77.4\% & 80.0\% & 86.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Clause-Wise F1 score accuracy.  
bridging the column-prediction and user-intention gap has the potential to enhance the overall accuracy of NLIDB systems. We extended the column module in SyntaxSQLNet, adjusting its encoder-decoder accordingly. We predicted the columns that were nearest to the user's intentions by adding column occurrences scores as an additional input feature to the model. This study shows that predicting user-intended columns can enhance overall accuracy.  
[PERSON] formulated the theoratical formalism and investigation. [PERSON] and [PERSON] contributed to the development and experimenting. [PERSON]-[PERSON] supervised the process and contributed with the funding aquisition. All authors discussed and analyzed the results and formulated the final menuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are openly available at github: [[https://yale-lily.github.io/spider](https://yale-lily.github.io/spider)]([https://yale-lily.github.io/spider](https://yale-lily.github.io/spider)), Reference number [36].  
All authors declare that they have no conflict of interest.