Reconsidering Degeneration of Token Embeddings with Definitions for Encoder-based Pre-trained Language Models

Ying Zhang; Dongyuan Li; Manabu Okumura

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arXiv · 2408.01308

Reconsidering Degeneration of Token Embeddings with Definitions for Encoder-based Pre-trained Language Models

Ying Zhang·Dongyuan Li·Manabu Okumura

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Abstract

Learning token embeddings based on token co-occurrence statistics has proven effective for both pre-training and fine-tuning in natural language processing. However, recent studies have pointed out that the distribution of learned embeddings degenerates into anisotropy (i.e., non-uniform distribution), and even pre-trained language models (PLMs) suffer from a loss of semantics-related information in embeddings for low-frequency tokens. This study first analyzes the fine-tuning dynamics of encoder-based PLMs and demonstrates their robustness against degeneration. On the basis of this analysis, we propose DefinitionEMB, a method that utilizes definitions to re-construct isotropically distributed and semantics-related token embeddings for encoder-based PLMs while maintaining original robustness during fine-tuning. Our experiments demonstrate the effectiveness of leveraging definitions from Wiktionary to re-construct such embeddings for two encoder-based PLMs: RoBERTa-base and BART-large. Furthermore, the re-constructed embeddings for low-frequency tokens improve the performance of these models across various GLUE and four text summarization datasets.

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BibTeX RIS

Ying Zhang, Dongyuan Li, Manabu Okumura. 2024-10-16. Reconsidering Degeneration of Token Embeddings with Definitions for Encoder-based Pre-trained Language Models. https://arxiv.org/abs/2408.01308

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