| Challenge: | In natural language processing, recurrent neural networks have a huge number of parameters. |
| Approach: | They propose a Bayesian sparsification technique which allows compressing RNNs dozens or hundreds of times without time-consuming hyperparameters tuning. |
| Outcome: | The proposed technique compresses the RNN dozens or hundreds of times without time-consuming hyperparameters tuning. |
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Deep Bayesian Natural Language Processing (P19-4)
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| Challenge: | Introduction to deep Bayesian learning for natural language addresses the fundamentals of statistical models and neural networks. |
| Approach: | This tutorial addresses the advances in deep Bayesian learning for natural language . it focuses on advanced Bayessian models and deep models . authors present case studies and domain applications to tackle different issues . |
| Outcome: | This tutorial focuses on advanced Bayesian models and deep models for natural language . case studies and domain applications are presented to tackle different issues in deep Bayessian processing, learning and understanding. |
Extreme Model Compression for On-device Natural Language Understanding (2020.coling-industry)
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| Challenge: | Xu and Sarikaya et al., 2014) perform word-embedding compression with NLU task learning . their approach achieves a compression rate of 97.4% with less than 3.7% degradation in predictive performance. |
| Approach: | They propose a task-aware, end-to-end compression approach that performs word-embedding compression with NLU task learning. |
| Outcome: | The proposed approach outperforms baselines and achieves 97.4% compression rate with less than 3.7% degradation in predictive performance. |
Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models (N19-1)
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| Challenge: | a technique that uses large corpus n-gram statistics as a regularizer for training a neural network LM on a smaller corpus is effective, and more time-efficient than training on ngrams. |
| Approach: | They propose a technique that uses large corpus n-gram statistics as a regularizer for training on a smaller corpus. |
| Outcome: | The proposed technique is effective and more time-efficient than training on a larger corpus. |
Prompt Compression for Large Language Models: A Survey (2025.naacl-long)
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| Challenge: | Current methods for improving LLM efficiency focus on optimizing the model itself, while prompt-centric methods focus on lowering the complexity of input. |
| Approach: | They propose to use prompt compression to optimize the compression encoder and combine hard and soft prompt methods to improve the efficiency of LLMs. |
| Outcome: | The proposed methods are categorized into hard prompt methods and soft prompt methods. |
Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization (2024.findings-emnlp)
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Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Zi Ye, Xinyu Duan, Zhefeng Wang, Kehai Chen, Min Zhang
| Challenge: | Large language models require a balance between efficiency and performance. |
| Approach: | They propose a low-rank compression technique that reduces non-essential parameters by decomposing weight matrices into products of two low-ranked matrici. |
| Outcome: | The proposed method outperforms existing pruning and low-rank compression techniques in maintaining model performance at the same compression ratio. |
Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models (2023.findings-eacl)
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| Challenge: | Recent transformer language models achieve outstanding results on many downstream tasks, but their enormous size often makes them impractical on memory-constrained devices. |
| Approach: | They propose an offline compression approach that reduces the complexity of the model by enabling collaboration between modules. |
| Outcome: | The proposed approach outperforms commonly used factorization-based offline compression methods on various NLP tasks. |
Efficient Methods for Natural Language Processing: A Survey (2023.tacl-1)
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Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz
| Challenge: | Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data, but using only scale to improve performance means resource consumption also grows. |
| Approach: | They propose to use data, time, storage, or energy to improve model performance. |
| Outcome: | The proposed methods and findings provide guidance for conducting NLP under limited resources and point towards promising research directions for developing more efficient methods. |
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (2025.emnlp-demos)
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| Challenge: | Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing are now available online. |
| Approach: | EMNLP 2025 conference on empirical methods in natural language processing held in Suzhou, china, on November 4-9, 2025. 77 papers accepted for inclusion in proceedings, resulting in 38% acceptance rate. |
| Outcome: | Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing are published . the conference accepted 77 papers, with a 38% acceptance rate . |
Compression of Generative Pre-trained Language Models via Quantization (2022.acl-long)
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| Challenge: | Existing methods to compress generative pre-trained language models fail on generative tasks due to homogeneous word embeddings and limited memory. |
| Approach: | They propose a token-level contrastive distillation method to learn distinguishable word embeddings and a module-wise dynamic scaling method to make quantizers adaptive to different modules. |
| Outcome: | The proposed method outperforms the state-of-the-art compression methods on generative PLMs by a clear margin. |
Bayesian Learning for Neural Dependency Parsing (N19-1)
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| Challenge: | Several approaches for dependency parsing in the small data regime have been proposed. |
| Approach: | They propose to use stochastic gradient Langevin dynamics to generate samples from the approximated posterior to overcome the computational and statistical costs of the approximate inference step. |
| Outcome: | The proposed model outperforms the biaffine model on 6 languages with less than 5k training instances and improves across five languages. |