Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions (2020)

Abstract: Existing persona-grounded dialog models often fail to capture simple implications of given persona descriptions, something which humans are able to do seamlessly. For example, state-of-the-art models cannot infer that interest in hiking might imply love for nature or longing for a break. In this paper, we propose to expand available persona sentences using existing commonsense knowledge bases and paraphrasing resources to imbue dialog models with access to an expanded and richer set of persona descriptions. Additionally, we introduce fine-grained grounding on personas by encouraging the model to make a discrete choice among persona sentences while synthesizing a dialog response. Since such a choice is not observed in the data, we model it using a discrete latent random variable and use variational learning to sample from hundreds of persona expansions. Our model outperforms competitive baselines on the Persona-Chat dataset in terms of dialog quality and diversity while achieving persona-consistent and controllable dialog generation.

## introduction persona-grounded dialog generation is a 'chit-chat' dialog setup where a dialog agent is expected to communicate based on a given profile @xcite . many recent works have focused on a popular benchmark dataset for this task: persona-chat @xcite ) that provides personas as a set of sentences along with each dialog (example in figure 1 ). however, a careful analysis of state-of-the-art (sota) models reveals that they often struggle to respond to contexts that do not closely match given persona sentences, even when the implications might be obvious to a human. for example, in figure 1 , the user asks an indirect question to the bot related to one of its persona sentences: i am an animal activist. sota1, which concatenates all persona sentences with dialog history and finetunes a pre-trained generative model (e.g. gpt2) @xcite , fails to infer implied commonsense from the dialog context and conditions on an incorrect persona. sota2, which separately selects a persona sentence given the dialog history @xcite manages to choose the correct persona but merely copies it as the final response. neither approach is in general capable of responding to context that goes beyond what is explicitly mentioned in the available persona sentences, which limits consistent and interesting conversation. the goal of our model is to understand that being 'an animal activist' may imply that the person wants 'to make a difference' via their activity towards animals and synthesizes a context-consistent and engaging response. in this paper, we focus on making personagrounded chatbots more consistent with personas and implicit dialog context. we present a framework to expand available persona sentences to their commonsense implications by using an existing commonsense knowledge base or paraphrasing resources (see section 3). we endow our dialog model with these expansions directly rather than requiring the model to learn them from scratch for being context-consistent. we find that expansions derived from a commonsense knowledge base are more useful to provide engaging contextual information compared to other expansion sources. we further propose a common sense and persona aligned chatbot foot_0 (compac) which models choices over the expanded persona set via a discrete latent random variable (see section 4) as fine-grained persona grounding. even though it is tractable to marginalize over all expansions, that would require a forward pass through the dialog generator for each outcome which is prohibitively slow during training. instead, to accommodate hundreds of persona expansions, we train the model by optimizing a lower bound on the log-likelihood. we use amortized variational inference by approximating the true posterior using an inference network that eventually provides useful inductive bias. particularly, we show that our bayesian formulation for the fine-grained persona grounding was essential as simply providing expanded knowledge does not help the model generate better responses. we also outperform competitive baselines in all dialog quality metrics as well as human evaluations which find compac to be engaging and coherent. we demonstrate that compac learns to be consistent with the dialog context with accurate persona grounding especially in the presence of commonsense expansions. finally, we show that our model can reflect a change in response generation when a grounding persona is modified, indicating the possibility of controllable generation. ## persona grounded dialog we use a popular benchmark dataset: persona-chat @xcite for our personagrounded dialog generation task. it contains 10,907 dialogs between pairs of speakers where each speaker follows their own persona; 968 dialogs are used for validation and 1,000 for testing. each speaker is described by 3-5 persona sentences. (e.g. 'i love the beach', 'my mother is a medical doctor'). out of 1,155 total unique personas, 100 are used for validation and 100 for testing. the task of persona-grounded dialog generation is: given a dialog history h and grounding persona sentences s, we must predict the next utterance x (summary of notations in ## persona expansion persona sentences used in persona-grounded dialogs are instances of world events that often imply real-world consequences or richer information. for example, 'i love surfing' naturally implies that the person might be 'adventurous' or 'loves the outdoors'. similarly, it also means that the person wants 'to go to the beach' regularly. inferring these expansions from the original fact is non-trivial without additional commonsense knowledge. @xcite found evidence that having human written interpretations of a persona sentence via rephrasing often helps in providing novel information in persona grounding. while obtaining such expansions by manual rewriting is expensive, here we explore two automatic ways to generate them at scale and separately evaluate them on the downstream dialog modeling task. ## common sense and persona aligned chatbot (compac) to infuse commonsense context in personagrounded dialog generation, we imbue our dialog model with the expanded persona set instead of only original personas s. but these persona expansions lead to hundreds of new sentences as opposed to only a few given persona sentences which makes it infeasible to encode using a single transformer, as was done in prior works @xcite . additionally, encoding all persona sentences as a single text input leads to a lack of interpretability i.e., it is not clear which persona sentence was used by the model in generating a particular response. instead, we propose compac: common sense and persona aligned chatbot that allows us to make a fine-grained choice of a persona sentence to generate the target response. let c denote a list of expended personas, derived from s (including s itself). we further add a null persona ∅ in c considering that some utterances can purely condition on the dialog context. we are interested in modeling the conditional p(x|h, c) = p(z|h, c)p(x|z, h, c) where @xmath0, 2, . . . , |c|} is a latent discrete random variable, unobserved in the data. given the dialog history h, first we sample a particular persona sentence c z from a prior network p θ (z|h) (see figure 3 ). next, as depicted in figure 3 , the dialog response x is sampled from a generator network p φ (x|h, c z ) by conditioning on the history h and chosen persona sentence c z . in the above described generative model, the latent variable z is a discrete random variable which ## experiments we conduct our experiments based on the following desiderata: (1) do persona expansions help to generate high quality and diverse responses? (2) does compac achieve accurate persona grounding given a dialog context? (3) does compac enable persona-consistent and controllable generation? hyperparameter details are in appendix §a. ## conclusion in this work, we showed that expanding persona sentences with commonsense helps a dialog model to generate high-quality and diverse personagrounded responses. moreover, we found that finegrained persona grounding is crucial to effectively condition on a large pool of commonsense persona expansions, which further provided additional controllability in conditional generation. while our expansions are limited by the performance of comet or paraphrase systems, we envision future work to train the dialog model endto-end along with the expansion generation. as future work, we would like extend the prior network to sample more than one persona sentences by expanding the sample space of the discrete random variable to generate more interesting responses.