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Register a new userKnowledge Distillation for Swedish NER models: A Search for Performance and Efficiency
تقطير المعرفة لنماذج NER السويدية: بحث عن الأداء والكفاءة
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تتمثل الوصفة الحالية لأداء نموذج أفضل داخل NLP في زيادة حجم نموذج البيانات والتدريب.في حين أن ذلك يعطينا نماذج مع نتائج رائعة بشكل متزايد، إلا أنها تجعل من الصعب تدريب ونشر نماذج أحدث ل NLP بسبب زيادة التكاليف الحاسوبية.ضغط النموذج هو مجال للبحث الذي يهدف إلى تخفيف هذه المشكلة.يشمل هذا المجال أساليب مختلفة تهدف إلى الحفاظ على أداء نموذج أثناء تقليل حجمها.واحدة من هذه الأسلوب هو تقطير المعرفة.في هذه المقالة، نحقق في تأثير تقطير المعرفة لنماذج التعرف على الكيان المسمى باللغة السويدية.نظهر أنه في حين أن بعض نماذج علامات التسلسل تستفيد من تقطير المعرفة، وليس كل النماذج تفعل.هذا يطالبنا بطرح أسئلة حول المواقف التي تنفجر المعرفة النماذج مفيدة.نحن أيضا السبب في تأثير تقطير المعرفة على التكاليف الحاسوبية.
The current recipe for better model performance within NLP is to increase model size and training data. While it gives us models with increasingly impressive results, it also makes it more difficult to train and deploy state-of-the-art models for NLP due to increasing computational costs. Model compression is a field of research that aims to alleviate this problem. The field encompasses different methods that aim to preserve the performance of a model while decreasing the size of it. One such method is knowledge distillation. In this article, we investigate the effect of knowledge distillation for named entity recognition models in Swedish. We show that while some sequence tagging models benefit from knowledge distillation, not all models do. This prompts us to ask questions about in which situations and for which models knowledge distillation is beneficial. We also reason about the effect of knowledge distillation on computational costs.
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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.
In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets. Instead of relying on one-hot annotation labels, our student summa
rization model is trained with guidance from a teacher which generates smoothed labels to help regularize training. Furthermore, to better model uncertainty during training, we introduce multiple noise signals for both teacher and student models. We demonstrate experimentally on three benchmarks that our framework boosts the performance of both pretrained and non-pretrained summarizers achieving state-of-the-art results.
Pretrained transformer-based encoders such as BERT have been demonstrated to achieve state-of-the-art performance on numerous NLP tasks. Despite their success, BERT style encoders are large in size and have high latency during inference (especially o
n CPU machines) which make them unappealing for many online applications. Recently introduced compression and distillation methods have provided effective ways to alleviate this shortcoming. However, the focus of these works has been mainly on monolingual encoders. Motivated by recent successes in zero-shot cross-lingual transfer learning using multilingual pretrained encoders such as mBERT, we evaluate the effectiveness of Knowledge Distillation (KD) both during pretraining stage and during fine-tuning stage on multilingual BERT models. We demonstrate that in contradiction to the previous observation in the case of monolingual distillation, in multilingual settings, distillation during pretraining is more effective than distillation during fine-tuning for zero-shot transfer learning. Moreover, we observe that distillation during fine-tuning may hurt zero-shot cross-lingual performance. Finally, we demonstrate that distilling a larger model (BERT Large) results in the strongest distilled model that performs best both on the source language as well as target languages in zero-shot settings.
Recent studies argue that knowledge distillation is promising for speech translation (ST) using end-to-end models. In this work, we investigate the effect of knowledge distillation with a cascade ST using automatic speech recognition (ASR) and machin
e translation (MT) models. We distill knowledge from a teacher model based on human transcripts to a student model based on erroneous transcriptions. Our experimental results demonstrated that knowledge distillation is beneficial for a cascade ST. Further investigation that combined knowledge distillation and fine-tuning revealed that the combination consistently improved two language pairs: English-Italian and Spanish-English.
Although pre-trained big models (e.g., BERT, ERNIE, XLNet, GPT3 etc.) have delivered top performance in Seq2seq modeling, their deployments in real-world applications are often hindered by the excessive computations and memory demand involved. For ma
ny applications, including named entity recognition (NER), matching the state-of-the-art result under budget has attracted considerable attention. Drawing power from the recent advance in knowledge distillation (KD), this work presents a novel distillation scheme to efficiently transfer the knowledge learned from big models to their more affordable counterpart. Our solution highlights the construction of surrogate labels through the k-best Viterbi algorithm to distill knowledge from the teacher model. To maximally assimilate knowledge into the student model, we propose a multi-grained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning.To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts. We further discuss ablation results to dissect our gains.
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