Characterizing the hyper-parameter space of LSTM language models for mixed context applications
This work addresses reproducibility concerns for applying deep learning models to real-world, mixed-context datasets, but it is incremental as it focuses on characterizing existing methods on new data.
The researchers investigated the sensitivity of LSTM language model hyper-parameters when applied to a novel code-mixed corpus, finding minimal sensitivity except for a few specific hyper-parameters.
Applying state of the art deep learning models to novel real world datasets gives a practical evaluation of the generalizability of these models. Of importance in this process is how sensitive the hyper parameters of such models are to novel datasets as this would affect the reproducibility of a model. We present work to characterize the hyper parameter space of an LSTM for language modeling on a code-mixed corpus. We observe that the evaluated model shows minimal sensitivity to our novel dataset bar a few hyper parameters.