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Register a new userLearning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora
التعلم السياقية الكلمات المتبقية الكلمة ومحاذاة لغات الموارد المنخفضة للغاية باستخدام Corpora الموازي
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نقترح نهجا جديدا لتعلم تضمين الكلمات المتبادلة عبر السياق بناء على كائن مواز صغير (E.G. بضع مئات من أزواج الجملة). تتمتع طريقتنا بدمج الكلمات عبر نموذج فك تشفير LSTM يترجم في وقت واحد وإعادة بناء جملة مدخلات. من خلال تقاسم المعلمات النموذجية بين لغات مختلفة، يدرك نموذجنا بشكل مشترك كلمة تضمين الكلمة في مساحة شائعة تبادل اللغات. نقترح أيضا الجمع بين وظائف الكلمة والكلمات الفرعية للاستفادة من أوجه التشابه الهجري عبر لغات مختلفة. نحن نؤدي تجاربنا على بيانات العالم الحقيقي من اللغات المهددة بالانقراض، وهي يونغينغ نا، Shipibo-Konibo، و Griko. تجاربنا على تحيزي المعجم الثنائي اللغة ومهام محاذاة الكلمات تظهر أن نموذجنا يفوق على الأساليب الحالية من قبل هامش كبير لمعظم أزواج اللغات. توضح هذه النتائج أنه على خلاف المعتقد الشائع، فإن نموذج الترجمة المشترك - ترميز الترميز مفيد لتعلم التمثيلات المتبادلة حتى في ظروف الموارد المنخفضة للغاية. علاوة على ذلك، يعمل نموذجنا أيضا بشكل جيد في ظروف الموارد العالية، وتحقيق الأداء الحديث في مهمة محاذاة الكلمة باللغة الألمانية.
We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common cross-lingual space. We also propose to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from endangered languages, namely Yongning Na, Shipibo-Konibo, and Griko. Our experiments on bilingual lexicon induction and word alignment tasks show that our model outperforms existing methods by a large margin for most language pairs. These results demonstrate that, contrary to common belief, an encoder-decoder translation model is beneficial for learning cross-lingual representations even in extremely low-resource conditions. Furthermore, our model also works well on high-resource conditions, achieving state-of-the-art performance on a German-English word-alignment task.
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Cross-lingual word embeddings (CLWEs) have proven indispensable for various natural language processing tasks, e.g., bilingual lexicon induction (BLI). However, the lack of data often impairs the quality of representations. Various approaches requiri
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