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Register a new userImproving Cross-Lingual Sentiment Analysis via Conditional Language Adversarial Nets
تحسين تحليل المعنويات عبر اللغات عبر شبكات لغة شرطية
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لقد قطع تحليل المعنويات شوطا طويلا لغات الموارد عالية الوزن بسبب توافر كورسا مشروح كبير.ومع ذلك، فإنه لا يزال يعاني من عدم وجود بيانات تدريب لغات الموارد المنخفضة.لمعالجة هذه المشكلة، نقترح شبكة الخصومة باللغة الشرطية (العشيرة)، وهي عبارة عن مبنى عصبي نهاية إلى نهائي لتحليل المشاعر المتبادلة دون إشراف عبر اللغات.تختلف العشيرة عن العمل المسبق في ذلك، حيث يسمح للتدريب الخصم بتصدر على كل من الميزات المستفادة وتنبؤ المعنويات، لزيادة التمييزي للتمثيل المستفاد في الإعداد المتبادل.تظهر النتائج التجريبية أن العشيرة تفوقت على الطرق السابقة في مجموعة بيانات مراجعة الأمازون متعددة المجالات متعددة اللغات.يتم إصدار شفرة المصدر لدينا في https://github.com/hemanthkandula/clan.
Sentiment analysis has come a long way for high-resource languages due to the availability of large annotated corpora. However, it still suffers from lack of training data for low-resource languages. To tackle this problem, we propose Conditional Language Adversarial Network (CLAN), an end-to-end neural architecture for cross-lingual sentiment analysis without cross-lingual supervision. CLAN differs from prior work in that it allows the adversarial training to be conditioned on both learned features and the sentiment prediction, to increase discriminativity for learned representation in the cross-lingual setting. Experimental results demonstrate that CLAN outperforms previous methods on the multilingual multi-domain Amazon review dataset. Our source code is released at https://github.com/hemanthkandula/clan.
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