  Conversational agents have become ubiquitous in assisting with daily tasks,
and are expected to possess human-like features. One such feature is lexical
entrainment (LE), a phenomenon in which speakers in human-human conversations
tend to naturally and subconsciously align their lexical choices with those of
their interlocutors, leading to more successful and engaging conversations. As
an example, if a digital assistant replies 'Your appointment for Jinling Noodle
Pub is at 7 pm' to the question 'When is my reservation for Jinling Noodle Bar
today?', it may feel as though the assistant is trying to correct the speaker,
whereas a response of 'Your reservation for Jinling Noodle Bar is at 7 pm'
would likely be perceived as more positive. This highlights the importance of
LE in establishing a shared terminology for maximum clarity and reducing
ambiguity in conversations. However, we demonstrate in this work that current
response generation models do not adequately address this crucial humanlike
phenomenon. To address this, we propose a new dataset, named MULTIWOZ-ENTR, and
a measure for LE for conversational systems. Additionally, we suggest a way to
explicitly integrate LE into conversational systems with two new tasks, a LE
extraction task and a LE generation task. We also present two baseline
approaches for the LE extraction task, which aim to detect LE expressions from
dialogue contexts.
