  Personas are useful for dialogue response prediction. However, the personas
used in current studies are pre-defined and hard to obtain before a
conversation. To tackle this issue, we study a new task, named Speaker Persona
Detection (SPD), which aims to detect speaker personas based on the plain
conversational text. In this task, a best-matched persona is searched out from
candidates given the conversational text. This is a many-to-many semantic
matching task because both contexts and personas in SPD are composed of
multiple sentences. The long-term dependency and the dynamic redundancy among
these sentences increase the difficulty of this task. We build a dataset for
SPD, dubbed as Persona Match on Persona-Chat (PMPC). Furthermore, we evaluate
several baseline models and propose utterance-to-profile (U2P) matching
networks for this task. The U2P models operate at a fine granularity which
treat both contexts and personas as sets of multiple sequences. Then, each
sequence pair is scored and an interpretable overall score is obtained for a
context-persona pair through aggregation. Evaluation results show that the U2P
models outperform their baseline counterparts significantly.
