  Most current language modeling techniques only exploit co-occurrence,
semantic and syntactic information from the sequence of words. However, a range
of information such as the state of the speaker and dynamics of the interaction
might be useful. In this work we derive motivation from psycholinguistics and
propose the addition of behavioral information into the context of language
modeling. We propose the augmentation of language models with an additional
module which analyzes the behavioral state of the current context. This
behavioral information is used to gate the outputs of the language model before
the final word prediction output. We show that the addition of behavioral
context in language models achieves lower perplexities on behavior-rich
datasets. We also confirm the validity of the proposed models on a variety of
model architectures and improve on previous state-of-the-art models with
generic domain Penn Treebank Corpus.
