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Register a new userAn Error Analysis Framework for Shallow Surface Realization
إطار تحليل الأخطاء لإعمال السطح الضحل
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مجردة المقاييس المستخدمة بشكل أساسي لتقييم نماذج توليد اللغة الطبيعية (NLG)، مثل Bleu أو Meteor، تفشل في تقديم معلومات حول تأثير العوامل اللغوية الأداء. التركيز على تحقيق السطح (SR)، ومهمة تحويل شجرة تبعية غير مرتبة في جملة رائعة، نقترح إطارا لتحليل الأخطاء الذي يسمح بتحديد ميزات الإدخال تؤثر على نتائج النماذج. يتكون هذا الإطار من عنصرين رئيسيين: (1) تحليلات الارتباط بين مجموعة واسعة من المقاييس النحوية ومقاييس الأداء القياسية و (2) مجموعة من التقنيات لتحديد البنيات النحوية تلقائيا والتي غالبا ما تحدث مع درجات أداء منخفضة. نوضح مزايا إطار الإطار الخاص بنا عن طريق إجراء تحليل الأخطاء في نتائج 174 يدير النظام المقدم إلى المهام المشتركة ل SR متعددة اللغات؛ نظهر أن دقة حافة التبعية ترتبط مع المقاييس التلقائية وبالتالي توفير أساس أكثر قابلية للتفسير للتقييم؛ ونقترح الطرق التي يمكن بها استخدام إطار عملنا لتحسين النماذج والبيانات. يتوفر الإطار في شكل مجموعة أدوات يمكن استخدامها على حد سواء من خلال منظمي الحملة لتوفير ملاحظات مفصلة، من التفسير اللغوي على حالة الفن في مجال الإرسال المتعدد اللغات، والباحثين الفرديين لتحسين النماذج ومجموعات البيانات
Abstract The metrics standardly used to evaluate Natural Language Generation (NLG) models, such as BLEU or METEOR, fail to provide information on which linguistic factors impact performance. Focusing on Surface Realization (SR), the task of converting an unordered dependency tree into a well-formed sentence, we propose a framework for error analysis which permits identifying which features of the input affect the models' results. This framework consists of two main components: (i) correlation analyses between a wide range of syntactic metrics and standard performance metrics and (ii) a set of techniques to automatically identify syntactic constructs that often co-occur with low performance scores. We demonstrate the advantages of our framework by performing error analysis on the results of 174 system runs submitted to the Multilingual SR shared tasks; we show that dependency edge accuracy correlate with automatic metrics thereby providing a more interpretable basis for evaluation; and we suggest ways in which our framework could be used to improve models and data. The framework is available in the form of a toolkit which can be used both by campaign organizers to provide detailed, linguistically interpretable feedback on the state of the art in multilingual SR, and by individual researchers to improve models and datasets.1
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The Reading Machine, is a parsing framework that takes as input raw text and performs six standard nlp tasks: tokenization, pos tagging, morphological analysis, lemmatization, dependency parsing and sentence segmentation. It is built upon Transition
Based Parsing, and allows to implement a large number of parsing configurations, among which a fully incremental one. Three case studies are presented to highlight the versatility of the framework. The first one explores whether an incremental parser is able to take into account top-down dependencies (i.e. the influence of high level decisions on low level ones), the second compares the performances of an incremental and a pipe-line architecture and the third quantifies the impact of the right context on the predictions made by an incremental parser.
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