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Register a new userIncorporating EDS Graph for AMR Parsing
دمج EDS الرسم البياني لحل AMR
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AMR (تمثيل المعنى التجريدي) و EDS (هياكل التبعية الابتدائية) هي تمثيلين لمعنى شعبيتين في NLP / NLU.AMR أكثر مجردة ومفاهيمية، في حين أن EDS هو أعلى مستوى منخفض، أقرب إلى الهياكل المعجمية للجمل المحددة.وبالتالي ليس من المستغرب أن تحليل EDS أسهل من تحليل عمرو.في هذا العمل، نفكر في استخدام معلومات من تحليل EDS للمساعدة في تحسين أداء تحليل عمرو.نعتمد محلل محلل ومقره انتقالي ويقترح بإضافة الرسوم البيانية EDS كيزات دلالة إضافية باستخدام تشفير رسم بياني يتكون من LSTM LETER وطبقة GCN.تبين نتائجنا التجريبية أن المعلومات الإضافية من تحليل EDS يعطي بالفعل دفعة إلى أداء محلل عمرو الأساسي المستخدمة في تجاربنا.
AMR (Abstract Meaning Representation) and EDS (Elementary Dependency Structures) are two popular meaning representations in NLP/NLU. AMR is more abstract and conceptual, while EDS is more low level, closer to the lexical structures of the given sentences. It is thus not surprising that EDS parsing is easier than AMR parsing. In this work, we consider using information from EDS parsing to help improve the performance of AMR parsing. We adopt a transition-based parser and propose to add EDS graphs as additional semantic features using a graph encoder composed of LSTM layer and GCN layer. Our experimental results show that the additional information from EDS parsing indeed gives a boost to the performance of the base AMR parser used in our experiments.
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