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تعلم توليف البيانات لتحليل الدلالي
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اكتسبت توليف البيانات لتحليل الدلالي اهتماما متزايدا مؤخرا. ومع ذلك، فإن معظم الطرق تتطلب قواعد يدوية (عالية الدقة) في عملية توليدها، مما يعوق استكشاف بيانات غير مرئية متنوعة. في هذا العمل، نقترح نموذجا عاما يتميز ببرنامج PCFG (غير العصبي) نماذج تكوين البرامج (E.G.، SQL)، ونموذج الترجمة المستندة إلى BART خرائط برنامج إلى كلام. نظرا لبساطة PCFG و BART المدربة مسبقا، يمكن تعلم نموذجنا التوليدي بكفاءة من البيانات الموجودة في متناول اليد. علاوة على ذلك، يؤدي التركيبات النمذجة بشكل صريح باستخدام PCFG إلى استكشاف أفضل لبرامج غير مرئية، وبالتالي توليد بيانات أكثر تنوعا. نقوم بتقييم طريقتنا في كل من الإعدادات داخل المجال والخروج من تحليل النص إلى SQL على المعايير القياسية للجهازية والعنكب العنكبوت، على التوالي. تبين نتائجنا التجريبية أن البيانات المركبة التي تم إنشاؤها من طرازنا يمكن أن تساعد بشكل كبير في محلل الدلالي يحقق تعميم أفضل أو مجال.
Synthesizing data for semantic parsing has gained increasing attention recently. However, most methods require handcrafted (high-precision) rules in their generative process, hindering the exploration of diverse unseen data. In this work, we propose a generative model which features a (non-neural) PCFG that models the composition of programs (e.g., SQL), and a BART-based translation model that maps a program to an utterance. Due to the simplicity of PCFG and pre-trained BART, our generative model can be efficiently learned from existing data at hand. Moreover, explicitly modeling compositions using PCFG leads to better exploration of unseen programs, thus generate more diverse data. We evaluate our method in both in-domain and out-of-domain settings of text-to-SQL parsing on the standard benchmarks of GeoQuery and Spider, respectively. Our empirical results show that the synthesized data generated from our model can substantially help a semantic parser achieve better compositional and domain generalization.
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