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Register a new userTranslate, then Parse! A Strong Baseline for Cross-Lingual AMR Parsing
ترجمة، ثم تحليل!خط أساس قوي لتخليص AMR عبر اللغات
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في تحليل التمثيل المعني المتبادل التجريدي (AMR)، يقوم الباحثون بتطوير النماذج التي تمارس طرزها من لغات مختلفة على الأمراض الأمريكية لالتقاط هياكلها الدلالية الأساسية: بالنظر إلى عقوبة بأي لغة، نهدف إلى التقاط المحتوى الدلالي الأساسي من خلال المفاهيم المتصلةأنواع متعددة من العلاقات الدلالية.الأساليب عادة ما تصل إلى بيانات التدريب الفضي الكبيرة لتعلم نموذج واحد قادر على مشروع الجمل غير الإنجليزية إلى AMRS.ومع ذلك، نجد أن خط الأساس البسيط يميل إلى التغاضي عنه: ترجمة الجمل إلى الإنجليزية وتستعرض AMR الخاص بهم مع محلل عمرو أحادي (ترجمة + تحليل، T + P).في هذه الورقة، نؤيد هذا الخط الأساسي البسيط من خطوتين، وتعزيزه بنظام NMT قوي ومحلل عمرو قوي.تظهر تجاربنا أن T + P يتفوق على نظام أحدث حديثة في جميع اللغات التي تم اختبارها: الألمانية والإيطالية والإسبانية وماندرين مع +14.6 و +12.6 و +14.3 ونقاط Smatch
In cross-lingual Abstract Meaning Representation (AMR) parsing, researchers develop models that project sentences from various languages onto their AMRs to capture their essential semantic structures: given a sentence in any language, we aim to capture its core semantic content through concepts connected by manifold types of semantic relations. Methods typically leverage large silver training data to learn a single model that is able to project non-English sentences to AMRs. However, we find that a simple baseline tends to be overlooked: translating the sentences to English and projecting their AMR with a monolingual AMR parser (translate+parse,T+P). In this paper, we revisit this simple two-step base-line, and enhance it with a strong NMT system and a strong AMR parser. Our experiments show that T+P outperforms a recent state-of-the-art system across all tested languages: German, Italian, Spanish and Mandarin with +14.6, +12.6, +14.3 and +16.0 Smatch points
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