Unsupervised Enrichment of Persona-grounded Dialog with Background Stories (2021)

Abstract: Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich. While persona-grounded dialog models are able to generate responses that follow a given persona, they often miss out on stating detailed experiences or events related to a persona, often leaving conversations shallow and dull. In this work, we equip dialog models with ‘background stories’ related to a persona by leveraging fictional narratives from existing story datasets (e.g. ROCStories). Since current dialog datasets do not contain such narratives as responses, we perform an unsupervised adaptation of a retrieved story for generating a dialog response using a gradient-based rewriting technique. Our proposed method encourages the generated response to be fluent (i.e., highly likely) with the dialog history, minimally different from the retrieved story to preserve event ordering and consistent with the original persona. We demonstrate that our method can generate responses that are more diverse, and are rated more engaging and human-like by human evaluators, compared to outputs from existing dialog models.

## introduction humans often rely on specific incidents and experiences while conversing in social contexts @xcite . responses from existing chitchat dialog agents often lack such specific details. to mitigate this, some prior work has looked into assigning personas to dialog agents @xcite . however, persona descriptions are often shallow and limited in scope, and while they lead to improvements response specificity, they still lack the level of detail with which humans share experiences. in this work, we propose methods to enrich dialog personas with relevant background events us-figure 1 : we enrich agent personas with 'background stories' from an existing corpus. we propose a gradient-based technique which encourages the generated response to be fluent with the dialog history, minimally different from the retrieved story, and consistent with the persona. the proposed approach leads to more specific and interesting responses. to adapt a retrieved narrative incident as a relevant background story, we use a decoding procedure which encourages the generated response to (1) be fluent with the dialog history, (2) be consistent with the original persona, and (3) be minimally different from the retrieved story. while fluency with dialog context is encouraged directly by the likelihood as per the underlying language model the remaining two constraints are incorporated via iterative updates to the decoder output distributions at inference time. our inference-time decoding method is different from the only recent effort by @xcite that leverages non-dialog data (forum comments, book snippets) as distant labels to train dialog systems with supervision. our contributions can be summarized as follows: ## unsupervised persona enrichment with background stories given dialog history h and persona c consisting of several (typically 3-5, example shown in figure 1 ) attributes, our goal is to construct a dialog response x. our underlying model is based on the discrete persona attribute choice model from @xcite . to generate a dialog utterance x, we first sample a persona attribute c ∼ p(c|h) conditioned on the dialog history h. x is then generated conditioned on the dialog history and the chosen persona attribute. the underlying dialog model's decoder is initialized with a pretrained gpt-2 model, and is fine-tuned on the personachat dataset @xcite . however, in our current setup, we also have to identify relevant background stories and use them to construct fluent responses at decoding time. therefore, we propose a different decoding procedure. to generate a response, we first sample a persona attribute c ∼ p(c|h). next we retrieve stories cor-responding to the persona attribute c (section 2.1). however, the underlying dialog model is trained to generate responses conditioned only on the dialog history and persona. to incorporate the retrieved story in the response, we perform gradient-based inference (section 2.2), that only assumes a left-toright language model trained on dialog context and responses, and the story is handled at decoding time in an unsupervised fashion. we refer to the proposed method as pabst (unsupervised persona enrichment with background stories). ## experiments we evaluate methods in terms of their capability to generate diverse, fluent and engaging responses. hyperparameters are noted in appendix §a. ## related work a desired impact of the proposed approach is increase in diversity of the generated responses. to tackle the issue of diversity in dialog model outputs, prior work has focused on decoding strategies such as diversity-promoting sampling @xcite ; training strategies such as discouraging undesirable responses via unlikelihood training @xcite ; model changes such as using stochastic variables @xcite ; and using external data such as forum data @xcite or external knowledge bases @xcite . in contrast to these, our proposed method generates responses with background stories using a gradientbased decoding approach. one of the steps in our proposed approach is to retrieve relevant stories from an external corpus. prior work has explored using retrieval of similar dialog instances as an initial step in improving response diversity and other human-like desiderata in dialog @xcite . distant supervision by using retrieved text snippets as pseudo responses has been explored in prior work @xcite . we use an external data source to improve dialog responses, a theme shared with some efforts in other tasks such as machine translation @xcite . the use of narrative text in dialog has been explored in prior work, mostly as a 'script' or template for conversation @xcite . we adapted a bert-based retrieval method @xcite in our case to retrieve relevant story given dialog context and use retrieved story in the decoding phase. gradient-based for text generation with soft constraints has been explored in prior work @xcite . @xcite focused on generating response which are consistent to given persona. differently, we use a gradientbased decoding to generate a dialog response while honoring constraints such as consistency to persona and similarity to retrieved story. ## conclusion we propose a method to enrich persona-grounded dialog with background stories at the inference time only using an existing corpus of non-conversational narratives-opening up new ways to generate enriched and engaging responses. one of the limitations of pabst is the assumption of the background story at every turn. as future work, we can include a decision step to decide if we need to incorporate a background story or not, given the dialog history. we can further explore ways to use retrieved stories over multiple turns instead of a single turn.