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Register a new userCome hither or go away? Recognising pre-electoral coalition signals in the news
تعال إلى هنا أو اذهب بعيدا؟الاعتراف بإشارات الائتلاف قبل الانتخابات في الأخبار
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في هذه الورقة، نقدم مهمة تنبؤ إشارة التحالف السياسي من النص، أي مهمة الاعتراف عن التغطية الإخبارية التي سبقت الانتخابات (الأمم المتحدة) استعداد الأحزاب السياسية لتشكيل ائتلاف حكومي.نحل مشكلتنا إلى مهمتين ذي صلة، ولكنها متبقية: (1) توقع ما إذا كان البيان المبلغ عنه من سياسي أو صحفي يشير إلى تحالف محتمل و (2) يتوقع قطبية الإشارة - أي ما إذا كان المتكلم فيصالح أو ضد الائتلاف.بالنسبة لهذا، نستكشف فوائد التعلم المتعدد المهام والتحقيق في ما هو الأنسب من الإعداد وصياغة المهمة لكل مهمة فرعية.نقيم نهجنا، بناء على مقالات جريدة مشفرة باليد، تغطي الانتخابات في ثلاث دول (أيرلندا وألمانيا والنمسا) ولغتين (الإنجليزية والألمانية).تظهر نتائجنا أن نهج التعلم متعدد المهام يمكن أن يؤدي إلى تحسين النتائج على خط أساسي قوي في مجال تحويل التحويل الأحادي.
In this paper, we introduce the task of political coalition signal prediction from text, that is, the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a government coalition. We decompose our problem into two related, but distinct tasks: (i) predicting whether a reported statement from a politician or a journalist refers to a potential coalition and (ii) predicting the polarity of the signal -- namely, whether the speaker is in favour of or against the coalition. For this, we explore the benefits of multi-task learning and investigate which setup and task formulation is best suited for each sub-task. We evaluate our approach, based on hand-coded newspaper articles, covering elections in three countries (Ireland, Germany, Austria) and two languages (English, German). Our results show that the multi-task learning approach can further improve results over a strong monolingual transfer learning baseline.
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