  This paper presents a novel open-domain dialogue generation model emphasizing
the differentiation of speakers in multi-turn conversations. Differing from
prior work that solely relies on the content of conversation history to
generate a response, we argue that capturing relative social relations among
utterances (i.e., generated by either the same speaker or different persons)
benefits the machine capturing fine-grained context information from a
conversation history to improve context coherence in the generated response.
Given that, we propose a speaker-aware Parallel Hierarchical Attentive
Encoder-Decoder (PHAED) model that aims to model each utterance with the
awareness of its speaker and contextual associations with the same speaker's
previous messages. Specifically, in a conversation involving two speakers, we
regard the utterances from one speaker as responses and those from the other as
queries. After understanding queries via our encoder with inner-query and
inter-query encodings, our decoder reuses the hidden states of previously
generated responses, instead of reconstructing these by the encoder, to
generate a new response. Our empirical results show that PHAED outperforms the
state-of-the-art in both automatic and human evaluations. Furthermore, our
ablation study shows that dialogue models with speaker tokens can generally
decrease the possibility of generating non-coherent responses regarding the
conversation context.
