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Register a new userTrainable Ranking Models to Evaluate the Semantic Accuracy of Data-to-Text Neural Generator
نماذج التصنيف القابلة للتدريب لتقييم الدقة الدلالية للمولدات العصبية للبيانات إلى النص
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في هذه الورقة، نقدم متري جديد يعتمد على تضمين التعاون على نماذج التصنيف القابلة للتدريب لتقييم الدقة الدلالية لمولدات البيانات النصية العصبية. هذا المتريات مناسب بشكل خاص لتقييم أداء مولد النص بشكل خاص بشكل خاص وتقييم فعليا عندما يمكن ربط الجداول بمراجع متعددة وقيم الجدول تحتوي على كلمات نصية نصية. نقدم أولا كيف يمكن للمرء تنفيذ ومزيد من التخصص المتخصص من خلال تدريب نماذج التصنيف الأساسية في مجموعة بيانات قانونية إلى نصية. نظهر كيف قد يوفر ذلك تقييما أكثر قوة من مخططات التقييم الأخرى في الإعدادات الصعبة باستخدام مجموعة بيانات تضم أي رسوم بين قيم الجدول ومراجعها. أخيرا، نقوم بتقييم قدرات تعميمها على مجموعة بيانات معروفة، و WEBNLG، بمقارنتها بالتقييم البشري ومقياس تم إدخاله مؤخرا بناء على الاستدلال اللغوي الطبيعي. بعد ذلك، توضح كيف تميز بشكل طبيعي، سواء من الناحية الكمية والنوعية والإغفالات والهلوسة.
In this paper, we introduce a new embedding-based metric relying on trainable ranking models to evaluate the semantic accuracy of neural data-to-text generators. This metric is especially well suited to semantically and factually assess the performance of a text generator when tables can be associated with multiple references and table values contain textual utterances. We first present how one can implement and further specialize the metric by training the underlying ranking models on a legal Data-to-Text dataset. We show how it may provide a more robust evaluation than other evaluation schemes in challenging settings using a dataset comprising paraphrases between the table values and their respective references. Finally, we evaluate its generalization capabilities on a well-known dataset, WebNLG, by comparing it with human evaluation and a recently introduced metric based on natural language inference. We then illustrate how it naturally characterizes, both quantitatively and qualitatively, omissions and hallucinations.
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While powerful pre-trained language models have improved the fluency of text generation models, semantic adequacy -the ability to generate text that is semantically faithful to the input- remains an unsolved issue. In this paper, we introduce a novel
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Data-to-text generation systems are trained on large datasets, such as WebNLG, Ro-toWire, E2E or DART. Beyond traditional token-overlap evaluation metrics (BLEU or METEOR), a key concern faced by recent generators is to control the factuality of the
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