  Discourse signals are often implicit, leaving it up to the interpreter to
draw the required inferences. At the same time, discourse is embedded in a
social context, meaning that interpreters apply their own assumptions and
beliefs when resolving these inferences, leading to multiple, valid
interpretations. However, current discourse data and frameworks ignore the
social aspect, expecting only a single ground truth. We present the first
discourse dataset with multiple and subjective interpretations of English
conversation in the form of perceived conversation acts and intents. We
carefully analyze our dataset and create computational models to (1) confirm
our hypothesis that taking into account the bias of the interpreters leads to
better predictions of the interpretations, (2) and show disagreements are
nuanced and require a deeper understanding of the different contextual factors.
We share our dataset and code at http://github.com/elisaF/subjective_discourse.
