| Challenge: | Existing studies have focused on the performance of structured prediction models, but they are often limited by the ambiguities of the reference policy. |
| Approach: | They propose to distill an ensemble of multiple models trained with different initializations into a single model and use it to explore the search space. |
| Outcome: | The proposed model outperforms the greedy models on two typical search-based structured prediction tasks and achieves 1.32 in LAS and 2.65 in BLEU over strong baselines. |
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| Challenge: | Modern neural networks do not always produce wellcalibrated predictions . post-hoc calibration methods require a held-out calibration dataset, which may not be available in all circumstances. |
| Approach: | They validate ensemble distillation framework for producing well-calibrated structured prediction models without the prohibitive inference-time cost of ensembles. |
| Outcome: | The proposed framework produces well-calibrated predictions without the prohibitive inference-time cost of ensembles. |
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor (2021.acl-long)
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Xinyu Wang, Yong Jiang, Zhaohui Yan, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
| Challenge: | Knowledge distillation is a technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). |
| Approach: | They propose a factorized form of the knowledge distillation objective for structured prediction which is tractable for many typical choices of the teacher and student models. |
| Outcome: | The proposed model is able to transfer knowledge between teacher and student models without loss of accuracy under four different scenarios. |
Distilling Structured Knowledge for Text-Based Relational Reasoning (2020.emnlp-main)
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| Challenge: | Existing text-based relational reasoning models lack a symbolic representation of text . performance gap between NLP models and structured models remains . |
| Approach: | They first pre-train a GNN on a reasoning task using structured inputs and then incorporate its knowledge into an NLP model. |
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Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection (2022.findings-emnlp)
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| Challenge: | Existing studies on knowledge distillation have shown that not all knowledge is necessary for learning a good student model. |
| Approach: | They propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation. |
| Outcome: | The proposed method outperforms several strong knowledge distillation baselines significantly on the GLUE datasets. |
Distillation of encoder-decoder transformers for sequence labelling (2023.findings-eacl)
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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. |
| Approach: | They propose a hallucination-free framework for sequence tagging that is especially suited for distillation. |
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Online Distilling from Checkpoints for Neural Machine Translation (N19-1)
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| Challenge: | Existing neural machine translation models have a deep structure with large amounts of parameters, making them hard to train. |
| Approach: | They propose an online method to generate a teacher model from checkpoints . they show steady improvement over a strong self-attention-based baseline system . |
| Outcome: | The proposed method improves on-the-fly on several datasets and language pairs. |
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. |
| Approach: | They propose a method that uses knowledge distillation to train multi-task neural networks that outperform or even match their single-task counterparts. |
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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. |
| Approach: | They propose to build an unlabeled transfer dataset to enable effective knowledge transfer . they hypothesize that this principled, general approach outperforms rule-based techniques . |
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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. |
| Approach: | They propose a one-teacher and multiple-student knowledge distillation approach to distill a deep pre-trained teacher model into multiple shallow student models with ensemble learning. |
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Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing knowledge distillation techniques for neural machine translation lack special treatment on the top-1 information, which is limiting the potential of KD. |
| Approach: | They propose a method to distill knowledge from top-1 predictions of teachers and a technique to infuse more additional knowledge by distilling on the data without ground-truth targets. |
| Outcome: | The proposed method outperforms the vanilla word-level KD and outperfies the existing methods on three different students with different capacity gaps. |