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Register a new userLearning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding
التعلم من مجموعات بيانات معدنية صاخبة متعددة من أجل فهم اللغة المنطوقة عبر اللغات أفضل
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يعرض عدم وجود بيانات تدريبية تحديا كبيرا لتحجيم فهم اللغة المنطوقة لغات الموارد المنخفضة.على الرغم من أن نهج تكبير البيانات المختلفة قد اقترحت توليف البيانات التدريبية في لغات مستهدفة منخفضة الموارد، فإن مجموعات البيانات المعززة غالبا ما تكون صاخبة، وبالتالي تعيق أداء نماذج SLU.في هذه الورقة نركز على تخفيف الضوضاء في البيانات المعززة.نقوم بتطوير نهج تدريب Denosising.يتم تدريب نماذج متعددة مع البيانات التي تنتجها الطرق المعززة المختلفة.توفر هذه النماذج إشارات الإشراف لبعضها البعض.تظهر النتائج التجريبية أن أسلوبنا تتفوق على الحالة القائمة من الفن الموجودة بمقدار 3.05 و 4.24 نقطة مئوية عن مجموعات بيانات قياسية على التوالي.سيتم تقديم الرمز مفتوح المصادر على جيثب.
Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models. In this paper we focus on mitigating noise in augmented data. We develop a denoising training approach. Multiple models are trained with data produced by various augmented methods. Those models provide supervision signals to each other. The experimental results show that our method outperforms the existing state of the art by 3.05 and 4.24 percentage points on two benchmark datasets, respectively. The code will be made open sourced on github.
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We present a systematic study on multilingual and cross-lingual intent detection (ID) from spoken data. The study leverages a new resource put forth in this work, termed MInDS-14, a first training and evaluation resource for the ID task with spoken d
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It is now established that modern neural language models can be successfully trained on multiple languages simultaneously without changes to the underlying architecture, providing an easy way to adapt a variety of NLP models to low-resource languages
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