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

Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances has a critical effect in practical use. Recent works have shown that using extra data and labels can improve the OOD detection performance, yet it could be costly to collect such data. This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection. Our method designs a novel domain-regularized module (DRM) to reduce the overconfident phenomenon of a vanilla classifier, achieving a better generalization in both cases. Besides, DRM can be used as a drop-in replacement for the last layer in any neural network-based intent classifier, providing a low-cost strategy for a significant improvement. The evaluation on four datasets shows that our method built on BERT and RoBERTa models achieves state-of-the-art performance against existing approaches and the strong baselines we created for the comparisons.

Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU

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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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

Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU

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

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