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Register a new userSemantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding
المحاذاة الدلالية مع التشابه المعايرة لضمان الجملة متعددة اللغات
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قياس درجة التشابه بين زوج من الجمل بلغات مختلفة هو المطلوبة الأساسية لأساليب تضمين الجملة متعددة اللغات. يتكون التنبؤ بدرجة التشابه من مهمتين فرعيتين، وهو تقييم التغلب غير المباشر واسترجاع الجملة متعددة اللغات. ومع ذلك، فإن الأساليب التقليدية قد تناولت أساسا واحدة فقط من المهام الفرعية، وبالتالي أظهرت عروضا متحيزا. في هذه الورقة، نقترح طريقة جديدة وطريقة قوية لتضمين الجملة متعددة اللغات، مما يدل على تحسين الأداء على كلا المهام الفرعية، وبالتالي مما يؤدي إلى تنبؤات قوية لدرجات التشابه متعددة اللغات. تتكون الطريقة المقترحة من جزأين: لتعلم التشابه الدلالي من الجمل في اللغة المحورية، ثم تمديد الهيكل الدلالي المستفاد لغات مختلفة. لمحاذاة الهياكل الدلالية عبر لغات مختلفة، نقدم شبكة مدرس وطالب. تقطير شبكة المعلم معرفة اللغة المحورية لغات مختلفة من شبكة الطلاب. أثناء التقطير، يتم تحديث معلمات شبكة المعلم مع المتوسط البطيء المتحرك. جنبا إلى جنب مع التقطير وتحديث المعلمة، يمكن محاذاة الهيكل الدلالي لشبكة الطالب مباشرة عبر لغات مختلفة مع الحفاظ على القدرة على قياس التشابه الدلالي. وبالتالي، فإن طريقة التدريب متعددة اللغات تدفع تحسين الأداء في تقييم التشابه متعدد اللغات. يحقق النموذج المقترح أداء الحديث في تقييم التشابه متعدد اللغات لعام 2017 بالإضافة إلى مهام فرعية، التي تمتد التقييم المتشابهات لأول مرة 2017 و Tatoeba متعددة اللغات متعددة اللغات في 14 لغة.
Measuring the similarity score between a pair of sentences in different languages is the essential requisite for multilingual sentence embedding methods. Predicting the similarity score consists of two sub-tasks, which are monolingual similarity evaluation and multilingual sentence retrieval. However, conventional methods have mainly tackled only one of the sub-tasks and therefore showed biased performances. In this paper, we suggest a novel and strong method for multilingual sentence embedding, which shows performance improvement on both sub-tasks, consequently resulting in robust predictions of multilingual similarity scores. The suggested method consists of two parts: to learn semantic similarity of sentences in the pivot language and then to extend the learned semantic structure to different languages. To align semantic structures across different languages, we introduce a teacher-student network. The teacher network distills the knowledge of the pivot language to different languages of the student network. During the distillation, the parameters of the teacher network are updated with the slow-moving average. Together with the distillation and the parameter updating, the semantic structure of the student network can be directly aligned across different languages while preserving the ability to measure the semantic similarity. Thus, the multilingual training method drives performance improvement on multilingual similarity evaluation. The suggested model achieves the state-of-the-art performance on extended STS 2017 multilingual similarity evaluation as well as two sub-tasks, which are extended STS 2017 monolingual similarity evaluation and Tatoeba multilingual retrieval in 14 languages.
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