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Register a new userAligning Cross-lingual Sentence Representations with Dual Momentum Contrast
محاذاة تمثيلات الجملة عبر اللغات مع التباين الزخم المزدوج
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في هذه الورقة، نقترحنا بمحاذاة تمثيلات الجملة من لغات مختلفة إلى مساحة تضمين موحدة، حيث يمكن حساب أوجه التشابه الدلالي (كل من الصليب اللغوي والأونولينغ) بمنتج نقطة بسيطة.نماذج اللغة المدربة مسبقا صقلها بشكل جيد مع مهمة تصنيف الترجمة.يستخدم العمل الحالي (فنغ وآخرون.، 2020) جمل داخل الدفعة مثل السلبيات، والتي يمكن أن تعاني من مسألة السلبيات السهلة.نحن نتكيف مع MOCO (هو et al.، 2020) لمزيد من تحسين جودة المحاذاة.نظرا لأن النتائج التجريبية تظهر، فإن تمثيلات الجملة التي تنتجها نموذجنا لتحقيق أحدث الولاية الجديدة في العديد من المهام، بما في ذلك البحث عن التشابه التشابه TATOEBA EN-ZH (Artetxe Andschwenk، 2019b)، Bucc En-Zh BiteXTالتشابه النصي في 7 مجموعات البيانات.
In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual) can be computed with a simple dot product. Pre-trained language models are fine-tuned with the translation ranking task. Existing work (Feng et al., 2020) uses sentences within the same batch as negatives, which can suffer from the issue of easy negatives. We adapt MoCo (He et al., 2020) to further improve the quality of alignment. As the experimental results show, the sentence representations produced by our model achieve the new state-of-the-art on several tasks, including Tatoeba en-zh similarity search (Artetxe andSchwenk, 2019b), BUCC en-zh bitext mining, and semantic textual similarity on 7 datasets.
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