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Register a new userMultilingual AMR Parsing with Noisy Knowledge Distillation
تحليل عمرو متعدد اللغات مع تقطير المعرفة صاخبة
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نحن ندرس تحليل عمرو متعدد اللغات من منظور تقطير المعرفة، حيث يكون الهدف هو تعلم وتحسين محلل عمرو متعدد اللغات باستخدام محلل إنجليزي موجود كمعلم لها.نحن تقيد استكشافنا في إعداد صارم متعدد اللغات: هناك نموذج واحد لتحليل جميع اللغات المختلفة بما في ذلك اللغة الإنجليزية.نحدد أن المدخلات الصاخبة والإخراج الدقيق هي مفتاح التقطير الناجح.جنبا إلى جنب مع التدريب المسبق الواسع، نحصل على محلل عمري الذي يتجنب عروضه جميع النتائج التي تم نشرها مسبقا على أربعة لغات أجنبية مختلفة، بما في ذلك الهوامش الألمانية والإسبانية والإيطالية والصينية، بواسطة هوامش كبيرة (تصل إلى 18.8 نقطة برائحة على الصينية وفي المتوسط 11.3نقاط smatch).يحقق محللنا أيضا أداء قابلا للمقارنة على اللغة الإنجليزية إلى أحدث المحللين باللغة الإنجليزية فقط.
We study multilingual AMR parsing from the perspective of knowledge distillation, where the aim is to learn and improve a multilingual AMR parser by using an existing English parser as its teacher. We constrain our exploration in a strict multilingual setting: there is but one model to parse all different languages including English. We identify that noisy input and precise output are the key to successful distillation. Together with extensive pre-training, we obtain an AMR parser whose performances surpass all previously published results on four different foreign languages, including German, Spanish, Italian, and Chinese, by large margins (up to 18.8 Smatch points on Chinese and on average 11.3 Smatch points). Our parser also achieves comparable performance on English to the latest state-of-the-art English-only parser.
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Knowledge Distillation (KD) is extensively used to compress and deploy large pre-trained language models on edge devices for real-world applications. However, one neglected area of research is the impact of noisy (corrupted) labels on KD. We present,
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To reduce a model size but retain performance, we often rely on knowledge distillation (KD) which transfers knowledge from a large teacher'' model to a smaller student'' model. However, KD on multimodal datasets such as vision-language tasks is relat
ively unexplored, and digesting multimodal information is challenging since different modalities present different types of information. In this paper, we perform a large-scale empirical study to investigate the importance and effects of each modality in knowledge distillation. Furthermore, we introduce a multimodal knowledge distillation framework, modality-specific distillation (MSD), to transfer knowledge from a teacher on multimodal tasks by learning the teacher's behavior within each modality. The idea aims at mimicking a teacher's modality-specific predictions by introducing auxiliary loss terms for each modality. Furthermore, because each modality has different saliency for predictions, we define saliency scores for each modality and investigate saliency-based weighting schemes for the auxiliary losses. We further study a weight learning approach to learn the optimal weights on these loss terms. In our empirical analysis, we examine the saliency of each modality in KD, demonstrate the effectiveness of the weighting scheme in MSD, and show that it achieves better performance than KD on four multimodal datasets.
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