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Register a new userIdentifying the Importance of Content Overlap for Better Cross-lingual Embedding Mappings
تحديد أهمية تداخل المحتوى للحصول على تعيينات تضمين أفضل عبر اللغات
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في هذا العمل، نقوم بتحليل أداء وخصائص نماذج تضمين الكلمة المتبقية التي تم إنشاؤها بواسطة أساليب المحاذاة المستندة إلى تعيين الخرائط.نحن نستخدم العديد من التدابير الخاصة بالجور وضمان التشابه للتنبؤ بعشرات BLI من تعيينات تضمين التضمين عبر اللغات على ثلاثة أنواع من كوربورا وثلاث أساليب تضمين و 55 زوجا للغة.تؤكد نتائجنا التجريبية على أنها بدلا من مجرد حجم، فإن مقدار المحتوى المشترك في Training Corpora ضروري.تتجلى هذه الظاهرة في ذلك) على الرغم من أحجام كوربوس الأصغر، باستخدام الأجزاء المقارنة فقط من ويكيبيديا لتدريب مساحات تضمين الأحادية المهتملة غالبا ما تكون أكثر فعالية من الاعتماد على جميع محتويات ويكيبيديا، 2) أصغر، في المقابلتعمل Wikipedia الأقل متنوعة في ويكيبيديا دائما أفضل بكثير كدولة تدريبية لتعيينات ثنائية اللغة من ويكيبيديا الإنجليزية المستخدمة في كل مكان.
In this work, we analyze the performance and properties of cross-lingual word embedding models created by mapping-based alignment methods. We use several measures of corpus and embedding similarity to predict BLI scores of cross-lingual embedding mappings over three types of corpora, three embedding methods and 55 language pairs. Our experimental results corroborate that instead of mere size, the amount of common content in the training corpora is essential. This phenomenon manifests in that i) despite of the smaller corpus sizes, using only the comparable parts of Wikipedia for training the monolingual embedding spaces to be mapped is often more efficient than relying on all the contents of Wikipedia, ii) the smaller, in return less diversified Spanish Wikipedia works almost always much better as a training corpus for bilingual mappings than the ubiquitously used English Wikipedia.
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