A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

Understand

Endowing dialogue systems with personas is essential to deliver more human-like conversations.

• However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora.
• This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data.
• In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories.