Finnish Language Modeling with Deep Transformer Models

• URL: http://arxiv.org/abs/2003.11562v2
• Date: Fri, 27 Mar 2020 10:02:24 GMT
• Title: Finnish Language Modeling with Deep Transformer Models
• Authors: Abhilash Jain, Aku Ruohe, Stig-Arne Gr\"onroos, Mikko Kurimo
• Abstract summary: We investigate the performance of the Transformer-BERT and Transformer-XL for the language modeling task. BERT achieves a pseudo-perplexity score of 14.5, which is the first such measure achieved as far as we know. Transformer-XL improves upon the perplexity score to 73.58 which is 27% better than the LSTM model.
• Score: 10.321630075961465
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Transformers have recently taken the center stage in language modeling after LSTM's were considered the dominant model architecture for a long time. In this project, we investigate the performance of the Transformer architectures-BERT and Transformer-XL for the language modeling task. We use a sub-word model setting with the Finnish language and compare it to the previous State of the art (SOTA) LSTM model. BERT achieves a pseudo-perplexity score of 14.5, which is the first such measure achieved as far as we know. Transformer-XL improves upon the perplexity score to 73.58 which is 27\% better than the LSTM model.

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