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Register a new userSemi-Automatic Construction of Text-to-SQL Data for Domain Transfer
تشييد نصف تلقائي لبيانات نص إلى SQL لنقل النطاق
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البيانات القوية وبأسعار معقولة في المجال هي أصل مرغوب فيه عند نقل المحللين الدلاليين المدربين إلى مجالات جديدة.نظرا لأن الأساليب السابقة لإنشاء مثل هذه البيانات نصف تلقائيا لا يمكن أن تتعامل مع تعقيد استفسارات SQL الواقعية، نقترح بناء استفسارات SQL عبر أخذ العينات التي تعتمد على السياق، وقدم مفهوم الموضوع.جنبا إلى جنب مع طريقة البناء الخاصة بنا SQL، نقترح خط أنابيب رواية من إنشاء بيانات النصوص شبه التلقائي Text-to-sql تغطي مساحة واسعة من استعلامات SQL.نظهر أن مجموعة البيانات التي تم إنشاؤها قابلة للمقارنة مع شروح الخبراء على طول أبعاد متعددة، وهي قادرة على تحسين أداء نقل المجال لمحلل سوتا الدلالي.
Strong and affordable in-domain data is a desirable asset when transferring trained semantic parsers to novel domains. As previous methods for semi-automatically constructing such data cannot handle the complexity of realistic SQL queries, we propose to construct SQL queries via context-dependent sampling, and introduce the concept of topic. Along with our SQL query construction method, we propose a novel pipeline of semi-automatic Text-to-SQL dataset construction that covers the broad space of SQL queries. We show that the created dataset is comparable with expert annotation along multiple dimensions, and is capable of improving domain transfer performance for SOTA semantic parsers.
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