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Register a new userSpanAlign: Efficient Sequence Tagging Annotation Projection into Translated Data applied to Cross-Lingual Opinion Mining
Spanalign: تسلسل تسلسل فعال عرض توضيحية في البيانات المترجمة المطبقة على تعدين الرأي عبر اللغات
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بعد أداء متزايد لأنظمة الترجمة الآلية العصبية، تتم الآن دراسة نموذج استخدام البيانات المترجمة تلقائيا للتكيف عبر اللغات في العديد من المجالات المعمارية.لا تزال القدرة على شرح المشروع بدقة، ومع ذلك، فإن هناك مشكلة في مهام علامات التسلسل حيث يجب توقع التوضيح بالمسافة الصحيحة.بالإضافة إلى ذلك، عندما تعني المهمة النص الناتج عن المستخدم صاخبة، يمكن أن تتأثر جودة الترجمة والترجمة التوضيحية.في هذه الورقة نقترحنا معالجة تسلسل تسلسل متعدد اللغات مع طريقة محاذاة سبين جديدة وتطبيقها على استخراج هدف الرأي من مراجعات العملاء.نظرا لأن توفير الاستدلال المناسبة، فإن البيانات المترجمة مع الإسقاط التلقائي التلقائي من المستوى التلقائي يمكن أن تسفر عن تحسينات التكيف عبر اللغات مقارنة بنقل الرصاص الصفر، وتعزيز البيانات مقارنة بناس خطي متعدد اللغات.
Following the increasing performance of neural machine translation systems, the paradigm of using automatically translated data for cross-lingual adaptation is now studied in several applicative domains. The capacity to accurately project annotations remains however an issue for sequence tagging tasks where annotation must be projected with correct spans. Additionally, when the task implies noisy user-generated text, the quality of translation and annotation projection can be affected. In this paper we propose to tackle multilingual sequence tagging with a new span alignment method and apply it to opinion target extraction from customer reviews. We show that provided suitable heuristics, translated data with automatic span-level annotation projection can yield improvements both for cross-lingual adaptation compared to zero-shot transfer, and data augmentation compared to a multilingual baseline.
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