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Register a new userAllocating Large Vocabulary Capacity for Cross-Lingual Language Model Pre-Training
تخصيص قدرة مفردات كبيرة لطراز اللغة عبر اللغات قبل التدريب
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بالمقارنة مع نماذج أحادية الأجل، تتطلب النماذج عبر اللغات عادة مفردات أكثر تعبيرية لتمثيل جميع اللغات بشكل كاف.نجد أن العديد من اللغات ممثلة تمثيلا ناقصا في نماذج اللغات الصليب الأخيرة بسبب قدرة المفردات المحدودة.تحقيقا لهذه الغاية، نقترح خوارزمية VOCAP لتحديد سعة المفردات المطلوبة لكل لغة.ومع ذلك، فإن زيادة حجم المفردات يبطئ بشكل كبير بسرعة ما قبل التدريب.من أجل معالجة المشكلات، نقترح أخذ العينات المستهدفة المستهدفة K-NN لتسريع SoftMax باهظة الثمن.تبين تجاربنا أن المفردات المتعددة اللغات المستفادة مع فوائد VOCAP نموذج اللغة المتبادلة قبل التدريب مسبقا.علاوة على ذلك، فإن أخذ العينات المستهدفة المستندة إلى K-NN تخفف الآثار الجانبية لزيادة حجم المفردات مع تحقيق أداء مماثل وسرعة ما قبل التدريب الأسرع.الرمز والمفردات متعددة اللغات المحددة متوفرة في https://github.com/bozheng-hit/vocapxlm.
Compared to monolingual models, cross-lingual models usually require a more expressive vocabulary to represent all languages adequately. We find that many languages are under-represented in recent cross-lingual language models due to the limited vocabulary capacity. To this end, we propose an algorithm VoCap to determine the desired vocabulary capacity of each language. However, increasing the vocabulary size significantly slows down the pre-training speed. In order to address the issues, we propose k-NN-based target sampling to accelerate the expensive softmax. Our experiments show that the multilingual vocabulary learned with VoCap benefits cross-lingual language model pre-training. Moreover, k-NN-based target sampling mitigates the side-effects of increasing the vocabulary size while achieving comparable performance and faster pre-training speed. The code and the pretrained multilingual vocabularies are available at https://github.com/bozheng-hit/VoCapXLM.
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