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Register a new userMetric Learning in Multilingual Sentence Similarity Measurement for Document Alignment
التعلم متري في قياس التشابه الجملة متعددة اللغات لمحاذاة المستند
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أظهرت مؤخرا تقنيات محاذاة المستندات بناء على تمثيلات جملة متعددة اللغات في مؤخرا حالة النتائج الفنية.ومع ذلك، تعتمد هذه التقنيات على تقنيات قياس المسافة غير المزعجة، والتي لا يمكن تغريمها بالمهمة في متناول اليد.في هذه الورقة، بدلا من تقنيات قياس المسافة غير المركبات هذه، فإننا نوظف التعلم المتري لاستخلاص قياسات المسافة الخاصة بالمهام.يتم إشراف هذه القياسات، مما يعني أن متري قياس المسافة يتم تدريبه باستخدام مجموعة بيانات متوازية.باستخدام مجموعة بيانات تنتمي إلى اللغة الإنجليزية، سنهالا، والتاميل، والتي تنتمي إلى ثلاث أسر لغوية مختلفة، نظهر أن مقاييس التعلم الخاصة بمهام المهام التي أشرفت على الفعالة تفوقت نظرائهم غير المعروضين، لمحاذاة المستندات.
Document alignment techniques based on multilingual sentence representations have recently shown state of the art results. However, these techniques rely on unsupervised distance measurement techniques, which cannot be fined-tuned to the task at hand. In this paper, instead of these unsupervised distance measurement techniques, we employ Metric Learning to derive task-specific distance measurements. These measurements are supervised, meaning that the distance measurement metric is trained using a parallel dataset. Using a dataset belonging to English, Sinhala, and Tamil, which belong to three different language families, we show that these task-specific supervised distance learning metrics outperform their unsupervised counterparts, for document alignment.
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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 eval
uation 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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