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Endowing dialogue systems with personas is essential to deliver morehuman-like conversations. However, this problem is still far from well exploreddue to the difficulties of both embodying personalities in natural languagesand the persona sparsity issue observed in most dialogue corpora. This paperproposes a pre-training based personalized dialogue model that can generatecoherent responses using persona-sparse dialogue data. In this method, apre-trained language model is used to initialize an encoder and decoder, andpersonal attribute embeddings are devised to model richer dialogue contexts byencoding speakers' personas together with dialogue histories. Further, toincorporate the target persona in the decoding process and to balance itscontribution, an attention routing structure is devised in the decoder to mergefeatures extracted from the target persona and dialogue contexts usingdynamically predicted weights. Our model can utilize persona-sparse dialoguesin a unified manner during the training process, and can also control theamount of persona-related features to exhibit during the inference process.Both automatic and manual evaluation demonstrates that the proposed modeloutperforms state-of-the-art methods for generating more coherent and personaconsistent responses with persona-sparse data.

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