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Register a new userIntegrating Transformers and Knowledge Graphs for Twitter Stance Detection
دمج المحولات ورسم البياني المعرفي للكشف عن موقف تويتر
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يستلزم الكشف عن الموقف (SD) تصنيف معنويات نص تجاه هدف معين، وهي مهمة فرعية ذات صلة لتحليل تعدين الرأي والوسائط الإعلامية الاجتماعية.وقد استكشفت الأعمال الحديثة تسريب المعرفة تكمل الكفاءة اللغوية والمعرفة الكامنة عن النماذج اللغوية الكبيرة المدربة مسبقا مع الرسوم البيانية المعرفة المهيكلة (KGS)، ومع ذلك فقد طبقت القليل من الأعمال هذه الأساليب إلى مهمة SD.في هذا العمل، نقوم أولا بتحقيق المعرفة ذات الصلة بالموقف على النماذج المدربة المستندة مسبقا للمحولات في إعداد تسديدة صفرية، مما يدل على المعرفة الحقيقية الكامنة في النماذج حول أهداف SD وحساستها للسياق.ثم ندرب وتقييم نماذج الكشف عن الموقف المخصب على المعرفة على مجموعة بيانات لموقف Twitter، وتحقيق أداء حديثة على حد سواء.
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 and 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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