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Many NLP applications, such as biomedical data and technical support, have10-100 million tokens of in-domain data and limited computational resources forlearning from it. How should we train a language model in this scenario? Mostlanguage modeling research considers either a small dataset with a closedvocabulary (like the standard 1 million token Penn Treebank), or the whole webwith byte-pair encoding. We show that for our target setting in English,initialising and freezing input embeddings using in-domain data can improvelanguage model performance by providing a useful representation of rare words,and this pattern holds across several different domains. In the process, weshow that the standard convention of tying input and output embeddings does notimprove perplexity when initializing with embeddings trained on in-domain data.

Improving Low Compute Language Modeling with In-Domain Embedding Initialisation