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Register a new userApproaching SMM4H with auto-regressive language models and back-translation
اقترب من SMM4H مع نماذج اللغة التراجع التلقائي والترجمة
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نحن تصف عروضنا إلى الطبعة السادسة من المهمة المشتركة للتطبيقات الاجتماعية للتطبيقات الصحية (SMM4H).شارك فريقنا (ognlp) في المهمة الفرعية: تصنيف تغريدات القضايا المحتملة للإبلاغ عنها الذاتي (المهمة 5).بالنسبة لتقديم طلباتنا، عملنا أنظمة بناء على نماذج المحولات التراجع التلقائي (XLNET) والترجمة الخلفية لموازنة DataSet.
We describe our submissions to the 6th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (OGNLP) participated in the sub-task: Classification of tweets self-reporting potential cases of COVID-19 (Task 5). For our submissions, we employed systems based on auto-regressive transformer models (XLNet) and back-translation for balancing the dataset.
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Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. To improve effectiveness of the available BT data, we introduce HintedBT
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