Distilling Multilingual Transformers into CNNs for Scalable Intent Classification (2022.emnlp-industry)
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| Challenge: | Existing multilingual models for voice assistants are limited by their prohibitive inference time and limited performance. |
| Approach: | They propose to distill and deploy multilingual Transformer models for voice assistants using a teacher-student framework that uses teacher-trained models to supervise student model training. |
| Outcome: | The proposed model outperforms a teacher model trained on unlabelled data and achieves equivalent performance. |
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| Challenge: | despite the strong trend in NLP to explore the use of large language models, there is still limited work evaluating prompting and decoding mechanisms for SL tasks. |
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A Comparative Analysis of Task-Agnostic Distillation Methods for Compressing Transformer Language Models (2023.emnlp-industry)
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| Challenge: | Large language models are often inefficient for real-world deployment due to expensive inference costs. |
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Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings (D19-61)
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| Challenge: | Recent research points to knowledge distillation as a potential solution for NLU tasks. |
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Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers (2020.emnlp-main)
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| Challenge: | Existing knowledge distillation techniques are not suitable for deep learning tasks due to memory constraints. |
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One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification (2022.coling-1)
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| Challenge: | Existing knowledge distillation models require large computing resources and long inference time to perform. |
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Natural Language Generation for Effective Knowledge Distillation (D19-61)
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| Challenge: | Knowledge distillation can transfer knowledge from deep language representation models to shallow word embedding-based neural networks. |
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Multi-teacher Distillation for Multilingual Spelling Correction (2023.emnlp-industry)
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| Challenge: | a multilingual spelling correction model is needed to meet the tight latency requirements of multilingual NLP . a monolingual teacher model is trained for each language/locale, and individual models are distilled into a single student model . |
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XtremeDistil: Multi-stage Distillation for Massive Multilingual Models (2020.acl-main)
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| Challenge: | Existing work on pre-trained language models focuses on reducing the size of these models into shallow ones. |
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MergeDistill: Merging Language Models using Pre-trained Distillation (2021.findings-acl)
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| Challenge: | Existing pre-trained multilingual language models often lack capacity and skewed data . this leads to inequitable representation of languages due to limited capacity and sub-optimal vocabularies. |
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BAM! Born-Again Multi-Task Networks for Natural Language Understanding (P19-1)
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| Challenge: | Existing methods to train multi-task neural networks outperform or even match their single-task counterparts are difficult to implement. |
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