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Register a new userSupertagging the Long Tail with Tree-Structured Decoding of Complex Categories
Supertagging الذيل الطويل مع فك التشفير منظم الأشجار الفئات المعقدة
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مجردة على الرغم من أن Supertaggers الحالي CCG يحقق دقة عالية على مجموعة اختبار WSJ القياسية، إلا أن القليل من الأنظمة تستخدم الهيكل الداخلي للفئات التي ستقود الاشتقاق النحوي أثناء التحليل.يتم اقتطاع التغذية تقليديا، وتخلص العديد من أنواع الفئات النادرة والمعقدة في الذيل الطويل.ومع ذلك، Supertags هي أنفسهم الأشجار.بدلا من التخلي عن علامات نادرة، نحقق في النماذج البناءة التي تمثل هيكلها الداخلي، بما في ذلك أساليب جديدة للتنبؤ منظم الأشجار.إن أفضل Tagger لدينا قادرة على استعادة جزء كبير من التبرعات الطويلة الذيل وحتى يولد فئات CCG التي لم يتم رؤيتها مطلقا في التدريب، مع تقارب الحالة السابقة للفن في دقة العلامات الشاملة مع عدد أقل من المعلمات.نحن مزيد من التحقيق في مدى تعميم النهج المختلفة لمجموعات التقييم خارج النطاق.
Abstract Although current CCG supertaggers achieve high accuracy on the standard WSJ test set, few systems make use of the categories' internal structure that will drive the syntactic derivation during parsing. The tagset is traditionally truncated, discarding the many rare and complex category types in the long tail. However, supertags are themselves trees. Rather than give up on rare tags, we investigate constructive models that account for their internal structure, including novel methods for tree-structured prediction. Our best tagger is capable of recovering a sizeable fraction of the long-tail supertags and even generates CCG categories that have never been seen in training, while approximating the prior state of the art in overall tag accuracy with fewer parameters. We further investigate how well different approaches generalize to out-of-domain evaluation sets.
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