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Article overview
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Generating Persona-Consistent Dialogue Responses Using Deep Reinforcement Learning | Mohsen Mesgar
; Edwin Simpson
; Yue Wang
; Iryna Gurevych
; | Date: |
30 Apr 2020 | Abstract: | Recent transformer-based open-domain dialogue agents are trained by reference
responses in a fully supervised scenario. Such agents often display
inconsistent personalities as training data potentially contain contradictory
responses to identical input utterances and no persona-relevant criteria are
used in their training losses. We propose a novel approach to train
transformer-based dialogue agents using actor-critic reinforcement learning. We
define a new reward function to assess generated responses in terms of persona
consistency, topic consistency, and fluency. Our reference-agnostic reward
relies only on a dialogue history and a persona defined by a list of facts.
Automatic and human evaluations on the PERSONACHAT dataset show that our
proposed approach increases the rate of persona-consistent responses compared
with its peers that are trained in a fully supervised scenario using reference
responses. | Source: | arXiv, 2005.0036 | Services: | Forum | Review | PDF | Favorites |
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