knowledge-grounded-dialogues/Spider/README.md

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

Spider is a large human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task (natural language interfaces for relational databases). It is released along with our EMNLP 2018 paper: Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. This repo contains all code for evaluation, preprocessing, and all baselines used in our paper. Please refer to the task site for more general introduction and the leaderboard.

Data Content and Format

Question, SQL, and Parsed SQL

Each file intrain.json and dev.json contains the following fields:

• question: the natural language question
• question_toks: the natural language question tokens
• db_id: the database id to which this question is addressed.
• query: the SQL query corresponding to the question.
• query_toks: the SQL query tokens corresponding to the question.
• sql: parsed results of this SQL query using process_sql.py. Please refer to parsed_sql_examples.sql in thepreprocess directory for the detailed documentation.

 {
        "db_id": "world_1",
        "query": "SELECT avg(LifeExpectancy) FROM country WHERE Name NOT IN (SELECT T1.Name FROM country AS T1 JOIN countrylanguage AS T2 ON T1.Code  =  T2.CountryCode WHERE T2.Language  =  \"English\" AND T2.IsOfficial  =  \"T\")",
        "query_toks": ["SELECT", "avg", "(", "LifeExpectancy", ")", "FROM", ...],
        "question": "What is average life expectancy in the countries where English is not the official language?",
        "question_toks": ["What", "is", "average", "life", ...],
        "sql": {
            "except": null,
            "from": {
                "conds": [],
                "table_units": [
                    ...
            },
            "groupBy": [],
            "having": [],
            "intersect": null,
            "limit": null,
            "orderBy": [],
            "select": [
                ...
            ],
            "union": null,
            "where": [
                [
                    true,
                    ...
                    {
                        "except": null,
                        "from": {
                            "conds": [
                                [
                                    false,
                                    2,
                                    [
                                    ...
                        },
                        "groupBy": [],
                        "having": [],
                        "intersect": null,
                        "limit": null,
                        "orderBy": [],
                        "select": [
                            false,
                            ...
                        "union": null,
                        "where": [
                            [
                                false,
                                2,
                                [
                                    0,
                                   ...
        }
    },

Tables

tables.json contains the following information for each database:

• db_id: database id
• table_names_original: original table names stored in the database.
• table_names: cleaned and normalized table names. We make sure the table names are meaningful. [to be changed]
• column_names_original: original column names stored in the database. Each column looks like: [0, "id"]. 0 is the index of table names in table_names, which is city in this case. "id" is the column name.
• column_names: cleaned and normalized column names. We make sure the column names are meaningful. [to be changed]
• column_types: data type of each column
• foreign_keys: foreign keys in the database. [3, 8] means column indices in the column_names. These two columns are foreign keys of two different tables.
• primary_keys: primary keys in the database. Each number is the index of column_names.

{
    "column_names": [
      [
        0,
        "id"
      ],
      [
        0,
        "name"
      ],
      [
        0,
        "country code"
      ],
      [
        0,
        "district"
      ],
      .
      .
      .
    ],
    "column_names_original": [
      [
        0,
        "ID"
      ],
      [
        0,
        "Name"
      ],
      [
        0,
        "CountryCode"
      ],
      [
        0,
        "District"
      ],
      .
      .
      .
    ],
    "column_types": [
      "number",
      "text",
      "text",
      "text",
         .
         .
         .
    ],
    "db_id": "world_1",
    "foreign_keys": [
      [
        3,
        8
      ],
      [
        23,
        8
      ]
    ],
    "primary_keys": [
      1,
      8,
      23
    ],
    "table_names": [
      "city",
      "sqlite sequence",
      "country",
      "country language"
    ],
    "table_names_original": [
      "city",
      "sqlite_sequence",
      "country",
      "countrylanguage"
    ]
  }

Databases

All table contents are contained in corresponding SQLite3 database files.

Citation

The dataset is annotated by 11 college students. When you use the Spider dataset, we would appreciate it if you cite the following:

@inproceedings{Yu&al.18c,
  title     = {Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task},
  author    = {Tao Yu and Rui Zhang and Kai Yang and Michihiro Yasunaga and Dongxu Wang and Zifan Li and James Ma and Irene Li and Qingning Yao and Shanelle Roman and Zilin Zhang and Dragomir Radev}
  booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
  address   = "Brussels, Belgium",
  publisher = "Association for Computational Linguistics",
  year      = 2018
}