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Register a new userControllable Semantic Parsing via Retrieval Augmentation
التحليل الدلالي قابل للتحكم عبر تكبير استرجاع
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في التطبيقات العملية للجدل الدلالي، نريد في كثير من الأحيان تغيير سلوك المحلل بسرعة، مثل تمكينه من التعامل مع الاستعلامات في مجال جديد، أو تغيير تنبؤاتها على بعض الاستفسارات المستهدفة. على الرغم من أنه يمكننا إدخال أمثلة تدريبية جديدة تظهر السلوك المستهدف، فإن آلية سن تغييرات السلوكية دون إعادة تدريب طراز باهظ الثمن سيكون أفضل. تحقيقا لهذه الغاية، نقترح المحلل الدلالي القابل للتحكم عبر استرجاع Exemplar (Casper). نظرا لاستعلام المدخلات، يسترد المحلل تحليل الخرزات ذات الصلة من مؤشر استرجاع، ويعززها إلى الاستعلام، ثم يطبق نموذج SEQ2SEQ Generative لإنتاج تحليل إخراج. تعمل EXEMPLARS كآلية مراقبة على النموذج العام العام: من خلال معالجة مؤشر الاسترجاع أو كيفية إنشاء الاستعلام المعزز، يمكننا معالجة سلوك المحلل المحلل. على مجموعة بيانات MTOP، بالإضافة إلى تحقيق أحدث من الفن في الإعداد القياسي، نظهر أن كاسبر يمكن أن تحليل الاستعلامات في مجال جديد، أو تكييف التنبؤ باتجاه الأنماط المحددة، أو التكيف مع مخططات الدلالات الجديدة دون الحاجة إلى الحاجة إلى مزيد من إعادة تدريب النموذج.
In practical applications of semantic parsing, we often want to rapidly change the behavior of the parser, such as enabling it to handle queries in a new domain, or changing its predictions on certain targeted queries. While we can introduce new training examples exhibiting the target behavior, a mechanism for enacting such behavior changes without expensive model re-training would be preferable. To this end, we propose ControllAble Semantic Parser via Exemplar Retrieval (CASPER). Given an input query, the parser retrieves related exemplars from a retrieval index, augments them to the query, and then applies a generative seq2seq model to produce an output parse. The exemplars act as a control mechanism over the generic generative model: by manipulating the retrieval index or how the augmented query is constructed, we can manipulate the behavior of the parser. On the MTOP dataset, in addition to achieving state-of-the-art on the standard setup, we show that CASPER can parse queries in a new domain, adapt the prediction toward the specified patterns, or adapt to new semantic schemas without having to further re-train the model.
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