  This work aims to build a dialogue agent that can weave new factual content
into conversations as naturally as humans. We draw insights from linguistic
principles of conversational analysis and annotate human-human conversations
from the Switchboard Dialog Act Corpus to examine humans strategies for
acknowledgement, transition, detail selection and presentation. When current
chatbots (explicitly provided with new factual content) introduce facts into a
conversation, their generated responses do not acknowledge the prior turns.
This is because models trained with two contexts - new factual content and
conversational history - generate responses that are non-specific w.r.t. one of
the contexts, typically the conversational history. We show that specificity
w.r.t. conversational history is better captured by Pointwise Conditional
Mutual Information ($\text{pcmi}_h$) than by the established use of Pointwise
Mutual Information ($\text{pmi}$). Our proposed method, Fused-PCMI, trades off
$\text{pmi}$ for $\text{pcmi}_h$ and is preferred by humans for overall quality
over the Max-PMI baseline 60% of the time. Human evaluators also judge
responses with higher $\text{pcmi}_h$ better at acknowledgement 74% of the
time. The results demonstrate that systems mimicking human conversational
traits (in this case acknowledgement) improve overall quality and more broadly
illustrate the utility of linguistic principles in improving dialogue agents.
