Transfer Learning for British Sign Language Modelling
This work addresses the challenge of modeling minority languages like sign languages, but it is incremental as it applies existing transfer learning methods to a new domain.
The paper tackled the problem of language modeling for British Sign Language, which suffers from a severe lack of data, by applying transfer learning techniques from English. The results showed an improvement in perplexity when using fine-tuning and layer substitution with stacked LSTM models.
Automatic speech recognition and spoken dialogue systems have made great advances through the use of deep machine learning methods. This is partly due to greater computing power but also through the large amount of data available in common languages, such as English. Conversely, research in minority languages, including sign languages, is hampered by the severe lack of data. This has led to work on transfer learning methods, whereby a model developed for one language is reused as the starting point for a model on a second language, which is less resourced. In this paper, we examine two transfer learning techniques of fine-tuning and layer substitution for language modelling of British Sign Language. Our results show improvement in perplexity when using transfer learning with standard stacked LSTM models, trained initially using a large corpus for standard English from the Penn Treebank corpus