论文标题
你问一个好问题吗？跨域问题意图分类基准，用于文本到SQL
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL
论文作者
论文摘要
神经模型在文本到SQL任务上取得了重大结果，其中大多数当前工作都假定所有输入问题都是合法的，并为任何输入生成了SQL查询。但是，在实际情况下，用户可以输入任何可能无法通过SQL查询来回答的文本。在这项工作中，我们提出了TriagesQL，这是第一个跨域文本到SQL问题意图分类基准，该基准要求模型将四种类型的无法回答的问题与可回答的问题区分开来。基线罗伯塔（Roberta）模型在测试集中获得了60％的F1分数，这表明需要进一步改进此任务。我们的数据集可从https://github.com/chatc/triagesql获得。
Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any input. However, in the real scenario, users can input any text that may not be able to be answered by a SQL query. In this work, we propose TriageSQL, the first cross-domain text-to-SQL question intention classification benchmark that requires models to distinguish four types of unanswerable questions from answerable questions. The baseline RoBERTa model achieves a 60% F1 score on the test set, demonstrating the need for further improvement on this task. Our dataset is available at https://github.com/chatc/TriageSQL.
