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Register a new userFine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling
تشفير ضبط الدقيقة لتحسين النمذجة النمذجة النمذجة النمذجة
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يمكن أن تصدر نماذج الموضوع العصبي أو استبدال مدخلات كيس الكلمات مع التمثيلات المستفادة من نماذج التنبؤ بكلمة التنبؤ المدربة مسبقا مسبقا. تتمثل إحدى فائدة واحدة عند استخدام التمثيلات من النماذج متعددة اللغات هي أنها تسهل نمذجة موضوع الصلاع اللاحق للصفر. ومع ذلك، في حين أنه لوحظ على نطاق واسع أن المدينات المدربة مسبقا يجب أن يتم ضبطها بشكل جيد لمهمة معينة، فليس من الواضح على الفور ما يجب أن يبدو الإشراف بهذه المهمة غير المزدوجة مثل نمذجة الموضوع. وبالتالي، نقترح عدة طرق لترميز التركيز الدقيق لتحسين كل من النمذجة النمذجة العصبية أحادية الألوان والصفرية. نحن نفكر في ضبط المهام الإضافية، بناء مهمة تصنيف موضوع جديد، دمج هدف تصنيف الموضوع بشكل مباشر في التدريب النموذجي للموضوع، واستمر التدريب قبل التدريب. نجد أن تمثيل تشفير الترميز بشكل جيد على تصنيف الموضوع وإدماج مهمة تصنيف الموضوع مباشرة في نمذجة موضوع يحسن جودة الموضوع، وأن تمثيل التشفير الدقيق في أي مهمة في أي مهمة هي أهم عامل لتسهيل النقل عبر اللغات.
Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is that they facilitate zero-shot polylingual topic modeling. However, while it has been widely observed that pre-trained embeddings should be fine-tuned to a given task, it is not immediately clear what supervision should look like for an unsupervised task such as topic modeling. Thus, we propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual neural topic modeling. We consider fine-tuning on auxiliary tasks, constructing a new topic classification task, integrating the topic classification objective directly into topic model training, and continued pre-training. We find that fine-tuning encoder representations on topic classification and integrating the topic classification task directly into topic modeling improves topic quality, and that fine-tuning encoder representations on any task is the most important factor for facilitating cross-lingual transfer.
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