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Register a new userAdversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.
التدريب الخصم للكشف عن موقف الأخبار: الاستفادة من إشارات من كوربوس متعدد الأنواع.
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يشكل التعميم الشامل مسألة مهمة للكشف عن الموقف (SD).في هذه الورقة القصيرة، نقوم بالتحقيق في SD الصلبة العدسة، حيث يتم الاستفادة من المعرفة من البيانات التي تم إنشاؤها من قبل المستخدم لتحسين الأخبار SD على أهداف غير مرئية أثناء التدريب.نقوم بتنفيذ شبكة مخدرة قائمة على Bert وتظهر تحسينات الأداء التجريبية على مجموعة من خطوط الأساس القوية.بالنظر إلى وفرة البيانات التي تم إنشاؤها من قبل المستخدم، والتي تكون أقل تكلفة بكثير لاسترداد وتعليقها من المقالات الإخبارية، فإن هذا يشكل اتجاها بحثا واعدا.
Cross-target generalization constitutes an important issue for news Stance Detection (SD). In this short paper, we investigate adversarial cross-genre SD, where knowledge from annotated user-generated data is leveraged to improve news SD on targets unseen during training. We implement a BERT-based adversarial network and show experimental performance improvements over a set of strong baselines. Given the abundance of user-generated data, which are considerably less expensive to retrieve and annotate than news articles, this constitutes a promising research direction.
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The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g
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Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis. Recent works have explored knowledge infusion supplementing the linguistic competence a
nd latent knowledge of large pre-trained language models with structured knowledge graphs (KGs), yet few works have applied such methods to the SD task. In this work, we first perform stance-relevant knowledge probing on Transformers-based pre-trained models in a zero-shot setting, showing these models' latent real-world knowledge about SD targets and their sensitivity to context. We then train and evaluate new knowledge-enriched stance detection models on two Twitter stance datasets, achieving state-of-the-art performance on both.
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