  Assisted text input techniques can save time and effort and improve text
quality. In this paper, we investigate how grounded and conditional extensions
to standard neural language models can bring improvements in the tasks of word
prediction and completion. These extensions incorporate a structured knowledge
base and numerical values from the text into the context used to predict the
next word. Our automated evaluation on a clinical dataset shows extended models
significantly outperform standard models. Our best system uses both
conditioning and grounding, because of their orthogonal benefits. For word
prediction with a list of 5 suggestions, it improves recall from 25.03% to
71.28% and for word completion it improves keystroke savings from 34.35% to
44.81%, where theoretical bound for this dataset is 58.78%. We also perform a
qualitative investigation of how models with lower perplexity occasionally fare
better at the tasks. We found that at test time numbers have more influence on
the document level than on individual word probabilities.
