غالبا ما يكون لدى السياسيين جداول أعمال أساسية عند الرد على الأحداث.تعكس الحجج في سياقات الأحداث المختلفة مجموعة متسقة إلى حد ما من جدول أعمال كيان معين.على الرغم من التطورات الأخيرة في نماذج اللغة المحددة مسبقا، فإن هذه التمثيلات النصية غير مصممة لالتقاط مثل هذه الأنماط الدقيقة.في هذه الورقة، نقترح نموذج قارئ ترخيص يتكون من وحدات التشفير والملحن، والتي تلتقط هذه المعلومات وتزود هذه المعلومات لتوليد تمثيلات أكثر فعالية للكيانات والقضايا والأحداث.هذه التمثيلات محكوم علي تغريدات، البيانات الصحفية، والقضايا، والمقالات الإخبارية، والكيانات المشاركة.يعالج النموذج لدينا العديد من المستندات في وقت واحد ويولد تمثيلات مؤلفة للحصول على كيانات متعددة على العديد من القضايا أو الأحداث.من خلال التحليل التجريبي النوعي والكمي، نوضح أن هذه التمثيلات ذات مغزى وفعال.
Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models, those text representations are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that captures and leverages such information to generate more effective representations for entities, issues, and events. These representations are contextualized by tweets, press releases, issues, news articles, and participating entities. Our model processes several documents at once and generates composed representations for multiple entities over several issues or events. Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.
References used
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