Optimizing Sentence Embedding with Pseudo-Labeling and Model Ensembles: A Hierarchical Framework for Enhanced NLP Tasks

Ziwei Liu; Qi Zhang; Lifu Gao

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arXiv · 2501.15876

Optimizing Sentence Embedding with Pseudo-Labeling and Model Ensembles: A Hierarchical Framework for Enhanced NLP Tasks

Ziwei Liu·Qi Zhang·Lifu Gao

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Abstract

Sentence embedding tasks are important in natural language processing (NLP), but improving their performance while keeping them reliable is still hard. This paper presents a framework that combines pseudo-label generation and model ensemble techniques to improve sentence embeddings. We use external data from SimpleWiki, Wikipedia, and BookCorpus to make sure the training data is consistent. The framework includes a hierarchical model with an encoding layer, refinement layer, and ensemble prediction layer, using ALBERT-xxlarge, RoBERTa-large, and DeBERTa-large models. Cross-attention layers combine external context, and data augmentation techniques like synonym replacement and back-translation increase data variety. Experimental results show large improvements in accuracy and F1-score compared to basic models, and studies confirm that cross-attention and data augmentation make a difference. This work presents an effective way to improve sentence embedding tasks and lays the groundwork for future NLP research.

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Ziwei Liu, Qi Zhang, Lifu Gao. 2025-01-27. Optimizing Sentence Embedding with Pseudo-Labeling and Model Ensembles: A Hierarchical Framework for Enhanced NLP Tasks. https://arxiv.org/abs/2501.15876

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