Learning Personas from Dialogue with Attentive Memory Networks

TL;DR

This paper presents neural models with attentive memory mechanisms to learn and infer character personas from dialogue, enabling applications like character clustering and similarity detection across movies.

Contribution

Introduces supervised neural models with multi-level attention and prior knowledge integration to learn persona embeddings from dialogue data.

Findings

Models effectively encode dialogue into persona representations.

Prior knowledge improves embedding quality.

Embeddings enable character similarity and clustering.

Abstract

The ability to infer persona from dialogue can have applications in areas ranging from computational narrative analysis to personalized dialogue generation. We introduce neural models to learn persona embeddings in a supervised character trope classification task. The models encode dialogue snippets from IMDB into representations that can capture the various categories of film characters. The best-performing models use a multi-level attention mechanism over a set of utterances. We also utilize prior knowledge in the form of textual descriptions of the different tropes. We apply the learned embeddings to find similar characters across different movies, and cluster movies according to the distribution of the embeddings. The use of short conversational text as input, and the ability to learn from prior knowledge using memory, suggests these methods could be applied to other domains.