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Register a new userUniteD-SRL: A Unified Dataset for Span- and Dependency-Based Multilingual and Cross-Lingual Semantic Role Labeling
United-SRL: مجموعة بيانات موحدة لعلامات الدلالية متعددة اللغات متعددة اللغات ومقرها التبعية
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وقد حافظت العلامات الدلالية المتعددة اللغات واللغات الدلالية (SRL) مؤخرا عن الاهتمام المتزايد لأن تقنيات تمثيل النص متعدد اللغات أصبحت أكثر فعالية ومتاحة على نطاق واسع. في حين أن العمل الحديث قد حقق النجاح المتزايد، فإن النتائج على معايير الذهب متعددة اللغات لا تزال غير قابلة للمقارنة بسهولة عبر اللغات، مما يجعل من الصعب فهم حيث نقف. على سبيل المثال، في Conll-2009، تتأثر المقارنات القياسية لمعيار SRL متعدد اللغات، وهي مقارنات لغة إلى لغوية بحقيقة أن كل لغة لها مجموعة بيانات خاصة بها والتي تختلف عن الآخرين في الحجم والمجالات ومجموعات من التسميات والإرشادات التوضيحية. في هذه الورقة، نتعلم هذه المشكلة واقترح United-SRL، معيار جديد لعطلة SRL متعددة اللغات والتبادلة والاعتماد على التبعية. يوفر United-SRL شرحا متوازيا من الخبراء باستخدام مخزون هيكل الوسائد المشترك، مما يسمح بالمقارنات المباشرة عبر اللغات والدراسات المشجعة على النقل عبر اللغات في SRL. نقوم بإصدار United-SRL V1.0 في https://github.com/sapienzanlp/united-srl.
Multilingual and cross-lingual Semantic Role Labeling (SRL) have recently garnered increasing attention as multilingual text representation techniques have become more effective and widely available. While recent work has attained growing success, results on gold multilingual benchmarks are still not easily comparable across languages, making it difficult to grasp where we stand. For example, in CoNLL-2009, the standard benchmark for multilingual SRL, language-to-language comparisons are affected by the fact that each language has its own dataset which differs from the others in size, domains, sets of labels and annotation guidelines. In this paper, we address this issue and propose UniteD-SRL, a new benchmark for multilingual and cross-lingual, span- and dependency-based SRL. UniteD-SRL provides expert-curated parallel annotations using a common predicate-argument structure inventory, allowing direct comparisons across languages and encouraging studies on cross-lingual transfer in SRL. We release UniteD-SRL v1.0 at https://github.com/SapienzaNLP/united-srl.
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