# Columns Occurrences Graph to Improve Column Prediction in Deep Learning [PERSON]
## 3 Related Work  
An NILIDB (natural language interface to database) system aims to cover the language hurdle between users and database systems. With NILIDB systems, non-technical users can also interact with the system without a knowledge of structured query languages. Therefore, NILIDB systems provide friendlier and greater access to relational databases for a broader range of users. Users query an NILIDB system in plain- or natural language text. The natural language question is then translated into a structured query language (SQL) query to be executed by the database engine and provide the user-intended results. Work by [1] is one of the early contributions to NILIDB [1], in which they presented an NILIDB task with brief examples describing problem statements and emphasized the importance of separating the linguistic task from the database information in their NILIDB task. To this end, syntax tree-based and semantic-based systems have been in the leading position, alongside intermediate representations of text and SQL languages. Until this point, the format of text query had been fixed, such as with fixed-syntax queries and menu-based systems, to limit associated semantic problems. Subsequently, [PERSON]. [2] proposed a method based on knowledge representation to separate the exact match tokens of natural language questions in the context to database elements. Moving further along the NILIDB research timeline, [PERSON]. [23] introduced the tree kernels concept in NILIDB systems. They integrated tree kernels to rank candidate queries. Further work in this area has belonged to two categories; pipeline-method NILIDBs [24] and neural-network NILIDBs. Deep learning is used most in research on neural-network NILIDBs. Deep learning NILIDBs can be categorized further into sequence-to-sequence learning [8] and sequence-to-set learning. For our work, we have adopted sequence-to-set learning. Sequence-to-set learning for text-to-SQL tasks was initially proposed to solve the 'order matters' issue [25]. The reward is calculated based on the whole ground-truth sequence compared to the gold-standard query for sequence-to-sequence structures. Sequence-to-set learning has been a fundamental approach for much work in NILIDB systems, in parallel with sequence-to-sequence learning, as the order of the columns does not matter in the context of execution results [8]. Later, however, in the timeline of NLIDB systems, semantic accuracy became one of the significant issues in processing text-to-SQL tasks, as it has a substantial contribution in overall accuracy. Researchers have attempted to resolve this issue in various ways, such as with slot-filling approaches [26; 27; 28], which separate the syntactic and semantic issues by dealing with the former via building syntax grammatically before predicting the schema elements to populate slots. This allowed models to focus more on the semantic issue while predicting columns and tables. Then, Ref. [12] proposed global reasoning by a gating GCN to encode the DB schema for improving the prediction of its elements. Schema linking is another way to tackle mismatch issues, and was recently used in IRNet [10; 29]. IRNet was extended to a sequence-based approach by [8]. They incorporated schema linking and intermediate representation into the baseline method. Schema linking, in IRNet, is done by simple string matching to identify the columns and tables mentioned in the question, labelling them with the column's information type. Although IRNet improved upon the baseline method, it is still not clear how schema linking impacts performance [10]. They separated the schema-linking task from the overall text-to-SQL task to analyze the impact of schema linking, showing that separating the schema entities from the questions and using placeholders instead allows the model to focus more on the syntactic component and improve overall query accuracy. Therefore, it is impactful on overall accuracy, but schema linking is not a perfect solution for semantic issues in text-to-SQL tasks.