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Register a new userTarget-Aware Data Augmentation for Stance Detection
تعظيم البيانات المستهدف للكشف عن الموقف
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الهدف من الكشف عن الموقف هو تحديد ما إذا كان مؤلف النص مؤلفا مؤلا، محايد أو ضد هدف محدد. على الرغم من التقدم الجوهري في هذه المهمة، فإن إحدى التحديات المتبقية هي ندرة التعليقات التوضيحية. يستخدم تكبير البيانات بشكل شائع لمعالجة ندرة التوضيحية عن طريق توليد المزيد من عينات التدريب. ومع ذلك، فإن الجمل المعزولة التي يتم إنشاؤها عن طريق الأساليب الحالية هي إما أقل تنوعا أو غير متسقة مع علامة الهدف والموقف المحدد. في هذه الورقة، صياغة تكبير البيانات للكشف عن الموقف كقوة نمذجة لغة مملحة مشروطة وزيادة مجموعة البيانات من خلال التنبؤ بالكلمة الملثمين المكيفة على كل من سياقها والجملة المساعدة التي تحتوي على معلومات الهدف والسمية. علاوة على ذلك، نقترح طريقة أخرى بسيطة ولكنها فعالة تولد الجملة المستهدفة من خلال استبدال هدف ذكر مع الآخر. تظهر النتائج التجريبية أن أسالكتنا المقترحة تتفوق بشكل كبير على أساليب التكبير السابقة على 11 أهداف.
The goal of stance detection is to identify whether the author of a text is in favor of, neutral or against a specific target. Despite substantial progress on this task, one of the remaining challenges is the scarcity of annotations. Data augmentation is commonly used to address annotation scarcity by generating more training samples. However, the augmented sentences that are generated by existing methods are either less diversified or inconsistent with the given target and stance label. In this paper, we formulate the data augmentation of stance detection as a conditional masked language modeling task and augment the dataset by predicting the masked word conditioned on both its context and the auxiliary sentence that contains target and label information. Moreover, we propose another simple yet effective method that generates target-aware sentence by replacing a target mention with the other. Experimental results show that our proposed methods significantly outperforms previous augmentation methods on 11 targets.
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