  Solving the challenges of automatic machine translation of Building
Automation System text metadata is a crucial first step in efficiently
deploying smart building applications. The vocabulary used to describe building
metadata appears small compared to general natural languages, but each term has
multiple commonly used abbreviations. Conventional machine learning techniques
are inefficient since they need to learn many different forms for the same
word, and large amounts of data must be used to train these models. It is also
difficult to apply standard techniques such as tokenisation since this commonly
results in multiple output tags being associated with a single input token,
something traditional sequence labelling models do not allow. Finite State
Transducers can model sequence-to-sequence tasks where the input and output
sequences are different lengths, and they can be combined with language models
to ensure a valid output sequence is generated. We perform a preliminary
analysis into the use of transducer-based language models to parse and
normalise building point metadata.
