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Register a new userCross-Lingual Word Embedding Refinement by $\ell_1$ Norm Optimisation
كلمة تبادل الكلمة تضمين التوصيل حسب التحسين $ \ ELL_1 $
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تزيين الكلمات المتبقية كلمة (CLWES) ترميز الكلمات من لغتين أو أكثر في مساحة مشتركة عالية الأبعاد التي تمثل ناقلات تمثل الكلمات ذات معنى مماثل (بغض النظر عن اللغة) عن كثب. تعلم الأساليب الحالية لبناء تعيينات CLWES عالية الجودة التي تقلل من وظيفة خسارة المعايير ℓ2. ومع ذلك، فقد ثبت أن هدف التحسين هذا هو حساسا للقيم المتطرفة. بناء على قاعدة مانهاتن الأكثر قوة (AKA. ℓ1 NORM) معيار الجيدة، تقترح هذه الورقة خطوة بسيطة بعد المعالجة لتحسين CLWES. ميزة هذا النهج هي أنه غير ملائم تماما للعملية التدريبية للفقر الأصلي ويمكن تطبيقها على نطاق واسع. يتم إجراء تجارب واسعة النطاق التي تنطوي على عشرة لغات متنوعة وموظفة مدربة على شريعة مختلفة. تظهر نتائج التقييم المستندة إلى تحريض المعجم الثنائي اللغة والتحويل عبر اللغات لمهام الاستدلال باللغة الطبيعية أن الصقل ℓ1 يتفوق بشكل كبير على أربعة خطوط خطوط خطوط خطوط أخرى في كل من الإعدادات الإشرافية غير المشرف. لذلك يوصى باعتماد هذه الاستراتيجية كمعيار لأساليب CLWE.
Cross-Lingual Word Embeddings (CLWEs) encode words from two or more languages in a shared high-dimensional space in which vectors representing words with similar meaning (regardless of language) are closely located. Existing methods for building high-quality CLWEs learn mappings that minimise the ℓ2 norm loss function. However, this optimisation objective has been demonstrated to be sensitive to outliers. Based on the more robust Manhattan norm (aka. ℓ1 norm) goodness-of-fit criterion, this paper proposes a simple post-processing step to improve CLWEs. An advantage of this approach is that it is fully agnostic to the training process of the original CLWEs and can therefore be applied widely. Extensive experiments are performed involving ten diverse languages and embeddings trained on different corpora. Evaluation results based on bilingual lexicon induction and cross-lingual transfer for natural language inference tasks show that the ℓ1 refinement substantially outperforms four state-of-the-art baselines in both supervised and unsupervised settings. It is therefore recommended that this strategy be adopted as a standard for CLWE methods.
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