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Register a new userExtend, don't rebuild: Phrasing conditional graph modification as autoregressive sequence labelling
تمديد، لا إعادة بناء: صيغة الرسم البياني الشرطي التعديل كوسسل تسلسل تلقائي
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أصبحت الرسوم البيانية المستمدة وتعديل الرسوم البيانية من نص اللغة الطبيعية تقنية أساس متعدد الاستخدامات لاستخراج المعلومات مع التطبيقات في العديد من المواد الفرعية، مثل بناء الرسم البياني الدلالي أو المعرفة الرسم البياني. استخدم العمل الأخير هذه التقنية لتعديل الرسوم البيانية المشهد (هو et al. 2020)، من خلال الترميز أولا الرسم البياني الأصلي ثم إنشاء واحد المعدلة بناء على هذا الترميز. في هذا العمل، نوضح أنه يمكننا زيادة الأداء بشكل كبير في هذه المشكلة من خلال صياغة ذلك كملحق رسم بياني بدلا من جيل الرسم البياني. نقترح النموذج الأول لمشكلة امتداد الرسم البياني الناتج استنادا إلى وضع العلامات التسلسل التلقائي. في مجموعات بيانات تعديل الرسم البياني المشهد، يؤدي هذا الصيانة إلى تحسينات في الدقة على أحدث من بين الفنين بين 13 نقطة مئوية 24 نقطة. علاوة على ذلك، نقدم بيانات جديدة مجموعة من المجال الطبي الطبيعي والتي لديها تقلب لغوي أكبر بكثير ورسم رسوم بيانية أكثر تعقيدا من مجموعات بيانات تعديل الرسم البياني المشهد. بالنسبة إلى مجموعة البيانات هذه، فشلت حالة الفن في التعميم، في حين أن طرازنا يمكن أن ينتج تنبؤات ذات مغزى.
Deriving and modifying graphs from natural language text has become a versatile basis technology for information extraction with applications in many subfields, such as semantic parsing or knowledge graph construction. A recent work used this technique for modifying scene graphs (He et al. 2020), by first encoding the original graph and then generating the modified one based on this encoding. In this work, we show that we can considerably increase performance on this problem by phrasing it as graph extension instead of graph generation. We propose the first model for the resulting graph extension problem based on autoregressive sequence labelling. On three scene graph modification data sets, this formulation leads to improvements in accuracy over the state-of-the-art between 13 and 24 percentage points. Furthermore, we introduce a novel data set from the biomedical domain which has much larger linguistic variability and more complex graphs than the scene graph modification data sets. For this data set, the state-of-the art fails to generalize, while our model can produce meaningful predictions.
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