Singular value decomposition (SVD) is one of the most popular compression methods that approximate a target matrix with smaller matrices. However, standard SVD treats the parameters within the matrix with equal importance, which is a simple but unrealistic assumption. The parameters of a trained neural network model may affect the task performance unevenly, which suggests non-equal importance among the parameters. Compared to SVD, the decomposition method aware of parameter importance is the more practical choice in real cases. Unlike standard SVD, weighed value decomposition is a non-convex optimization problem that lacks a closed-form solution. 
We systematically investigated multiple optimization strategies to tackle the problem and examined our method by compressing Transformer-based language models.
Further, we designed a metric to predict when the SVD may introduce a significant performance drop, for which our method can be a rescue strategy.
The extensive evaluations demonstrate that our method can perform better than current SOTA methods in compressing Transformer-based language models.

Numerical Optimizations for Weighted Low-rank Estimation on Language Models

Domain classification is the fundamental task in natural language understanding (NLU), which often requires fast accommodation to new emerging domains. We proposed a hyperparameter-free continual learning model for text data that can stably produce high performance under various environments. We utilize Fisher information to select exemplars that can “record” key information of the original model. Also, a novel scheme called dynamical weight consolidation is proposed to enable hyperparameter-free learning during the retrain process. Extensive experiments demonstrate baselines provide fluctuated performance which makes them useless in practice. On the contrary, our proposed model significantly and consistently outperforms the best state-of-the-art method by up to 20% in average accuracy, and each of its components contributes effectively to over- all performance.

Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding

O20 Machine Learning for NLP

technical paper

## Welcome to EMNLP 2022!
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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

**Session Chair: ** Srini Iyer

8A-Oral: Machine Learning for NLP: Language Modeling and Sequence to Sequence Models

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Numerical Optimizations for Weighted Low-rank Estimation on Language Models

Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding

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