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    <abstract>Word embeddings and pre-trained language models have become essential technical elements in natural language processing. While the general practice is to use or fine-tune publicly available models, there are significant advantages in creating or pre-training unique models that match the domain. The performance of the models degrades as language changes or evolves continuously, but the high cost of model building inhibits regular re-training, especially for the language models. This study proposes an efficient way to detect time-series performance degradation of word embeddings and pre-trained language models by calculating the degree of semantic shift. Monitoring performance through the proposed method supports decision-making as to whether a model should be re-trained. The experiments demonstrated that the proposed method can identify time-series performance degradation in two datasets, Japanese and English. The source code is available at https://github.com/Nikkei/semantic-shift-stability.</abstract>
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%0 Conference Proceedings
%T Semantic Shift Stability: Efficient Way to Detect Performance Degradation of Word Embeddings and Pre-trained Language Models
%A Ishihara, Shotaro
%A Takahashi, Hiromu
%A Shirai, Hono
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F ishihara-etal-2022-semantic
%X Word embeddings and pre-trained language models have become essential technical elements in natural language processing. While the general practice is to use or fine-tune publicly available models, there are significant advantages in creating or pre-training unique models that match the domain. The performance of the models degrades as language changes or evolves continuously, but the high cost of model building inhibits regular re-training, especially for the language models. This study proposes an efficient way to detect time-series performance degradation of word embeddings and pre-trained language models by calculating the degree of semantic shift. Monitoring performance through the proposed method supports decision-making as to whether a model should be re-trained. The experiments demonstrated that the proposed method can identify time-series performance degradation in two datasets, Japanese and English. The source code is available at https://github.com/Nikkei/semantic-shift-stability.
%U https://aclanthology.org/2022.aacl-main.17
%P 205-216

Markdown (Informal)

[Semantic Shift Stability: Efficient Way to Detect Performance Degradation of Word Embeddings and Pre-trained Language Models](https://aclanthology.org/2022.aacl-main.17) (Ishihara et al., AACL-IJCNLP 2022)

• Semantic Shift Stability: Efficient Way to Detect Performance Degradation of Word Embeddings and Pre-trained Language Models (Ishihara et al., AACL-IJCNLP 2022)

ACL

• Shotaro Ishihara, Hiromu Takahashi, and Hono Shirai. 2022. Semantic Shift Stability: Efficient Way to Detect Performance Degradation of Word Embeddings and Pre-trained Language Models. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 205–216, Online only. Association for Computational Linguistics.