We lifted 5 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| hirupert/sede | canonical | 0 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| add_schema_description | Not yet run | hirupert/sede/src/preprocessing/preprocess.py code served (permissive licence) · get_code("4d680eba792d9d58") |
| clean_str | Not yet run | hirupert/sede/src/preprocessing/preprocess.py code served (permissive licence) · get_code("f523c4d648a7a510") |
| preprocess | Not yet run | hirupert/sede/src/utils.py code served (permissive licence) · get_code("34a9ad8ddd29afcd") |
| remove_sql_comm | Not yet run | hirupert/sede/src/utils.py code served (permissive licence) · get_code("8fe7ef05eb8ff56f") |
| split_sql_text | Not yet run | hirupert/sede/src/utils.py code served (permissive licence) · get_code("8ac5261604f67eef") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Most available semantic parsing datasets, comprising of pairs of natural utterances and logical forms, were collected solely for the purpose of training and evaluation of natural language understanding systems. As a result, they do not contain any of the richness and variety of natural-occurring utterances, where humans ask about data they need or are curious about. In this work, we release SEDE, a dataset with 12,023 pairs of utterances and SQL queries collected from real usage on the Stack Exchange website. We show that these pairs contain a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset, propose an evaluation metric based on comparison of partial query clauses that is more suitable for real-world queries, and conduct experiments with strong baselines, showing a large gap between the performance on SEDE compared to other common datasets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.05006")
get_code_for_paper("2106.05006")
have("2106.05006")
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