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Register a new userParameter Space Factorization for Zero-Shot Learning across Tasks and Languages
المعلمة تعامل الفضاء للتعلم صفر النار عبر المهام واللغات
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مجردة معظم مجموعات مهام NLP والأصناف اللغوية تفتقر إلى أمثلة في المجال للتدريب الخاضع للإشراف بسبب قلة البيانات المشروحة. كيف يمكن النماذج العصبية أن تجعل تعميمات فعالة للعينة من مجموعات لغات المهام مع البيانات المتاحة للموارد المنخفضة؟ في هذا العمل، نقترح نموذجا إيلائيا بايزيا لمساحة المعلمات العصبية. نفترض أن هذه المساحة يمكن أن تعصبها في متغيرات كامنة لكل لغة وكل مهمة. نحن نستنتج المشتريات حول هذه المتغيرات الكامنة بناء على بيانات من مجموعات لغة المهام المشاهدة من خلال الاستدلال المتغيرات. وهذا يتيح تصنيف صفري بالرصاص على مجموعات غير مرئية في وقت التنبؤ. على سبيل المثال، نظرا لبيانات التدريب للتعرف على الكيان المسمى (NER) في الفيتنامية ولليزة جزء من الكلام (POS) (POS) في Wolof، يمكن أن يؤدي نموذجنا إلى إجراء تنبؤات دقيقة ل NER في Wolof. على وجه الخصوص، نقوم بتجربة عينة متنوعة من 33 لغة من 4 قارات و 11 أسرة، وإظهار أن نموذجنا ينتج عنه نتائج قابلة للمقارنة أو أفضل من أساليب التحويل المتبادلة الصفرية من بين الفن. يتوفر الكود الخاص بنا في github.com/cambridgeltl/parameter-factorization.
Abstract Most combinations of NLP tasks and language varieties lack in-domain examples for supervised training because of the paucity of annotated data. How can neural models make sample-efficient generalizations from task--language combinations with available data to low-resource ones? In this work, we propose a Bayesian generative model for the space of neural parameters. We assume that this space can be factorized into latent variables for each language and each task. We infer the posteriors over such latent variables based on data from seen task--language combinations through variational inference. This enables zero-shot classification on unseen combinations at prediction time. For instance, given training data for named entity recognition (NER) in Vietnamese and for part-of-speech (POS) tagging in Wolof, our model can perform accurate predictions for NER in Wolof. In particular, we experiment with a typologically diverse sample of 33 languages from 4 continents and 11 families, and show that our model yields comparable or better results than state-of-the-art, zero-shot cross-lingual transfer methods. Our code is available at github.com/cambridgeltl/parameter-factorization.
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