Computer Science > Computation and Language
[Submitted on 14 Mar 2020 (v1), last revised 27 Mar 2020 (this version, v2)]
Title:Finnish Language Modeling with Deep Transformer Models
View PDFAbstract: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.
Submission history
From: Abhilash Jain [view email][v1] Sat, 14 Mar 2020 15:12:03 UTC (32 KB)
[v2] Fri, 27 Mar 2020 10:02:24 UTC (33 KB)
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