Response Generation in Longitudinal Dialogues: Which Knowledge
  Representation Helps?
Seyed Mahed Mousavi Simone Caldarella Giuseppe Riccardi
Longitudinal Dialogues (LD) are the most challenging type of conversation for
human-machine dialogue systems. LDs include the recollections of events,
personal thoughts, and emotions specific to each individual in a sparse
sequence of dialogue sessions. Dialogue systems designed for LDs should
uniquely interact with the users over multiple sessions and long periods of
time (e.g. weeks), and engage them in personal dialogues to elaborate on their
feelings, thoughts, and real-life events. In this paper, we study the task of
response generation in LDs. We evaluate whether general-purpose Pre-trained
Language Models (PLM) are appropriate for this purpose. We fine-tune two PLMs,
GePpeTto (GPT-2) and iT5, using a dataset of LDs. We experiment with different
representations of the personal knowledge extracted from LDs for grounded
response generation, including the graph representation of the mentioned events
and participants. We evaluate the performance of the models via automatic
metrics and the contribution of the knowledge via the Integrated Gradients
technique. We categorize the natural language generation errors via human
evaluations of contextualization, appropriateness and engagement of the user.