The Wizard of Curiosities: Enriching Dialogues with Fun Facts (2023)

Abstract: Introducing curiosities in a conversation is a way to teach something new to the person in a pleasant and enjoyable way. Enriching dialogues with contextualized curiosities can improve the users’ perception of a dialog system and their overall user experience. In this paper, we introduce a set of curated curiosities, targeting dialogues in the cooking and DIY domains. In particular, we use real human-agent conversations collected in the context of the Amazon Alexa TaskBot challenge, a multimodal and multi-turn conversational setting. According to an A/B test with over 1000 conversations, curiosities not only increase user engagement, but provide an average relative rating improvement of 9.7%.

## introduction the concept of curiosity has for decades been debated by neuroscientists and psychologists. according to @xcite , it can be framed into two research views: (1) curiosity as a natural impulse for seeking extended cognition; and (2) a phenomenon related to exploring, playing, learning, and the desire for information. @xcite went even further, meditating about how humans had inherently a special type of curiosity, an epistemic curiosity, meaning that above the exploration and information-seeking need, humans also strive for knowledge. multimodal conversational task assistants @xcite seek to guide users in accomplishing complex tasks (e.g. "cooking a strawberry pie" or "fixing a broken chair"), in an objective, concise, and engaging manner. naturally, conversations are rich in knowledge and senses, that are transmitted to users in a dosed manner, towards a successful completion of the task, such that at all phases, knowledge complexity is managed. from the user's perspective, executing a task can be cognitively demanding, potentially involving learning new procedures, using new tools, and following complex task instructions. thus, conversational assistants should not only ensure a smooth completion of the tasks but also seek to make the task execution a pleasant and entertaining experience that appeals to human senses and curiosity @xcite . to that end, we propose to enrich conversational task assistants with contextualized fun facts, exploiting humans' curiosity-driven informationseeking traits @xcite . as seen in the work of @xcite , trivia facts have a positive impact on conversations with virtual agents, if used correctly. hence, in this paper, when dialoguing about a complex task, the user is guided through a sequence of steps as shown in the example in appendix a. any attempt to fruitfully extend a conversation flow must be done with care. thus, dialog curiosities should be used as a dialogenriching element that seeks to maximize user satisfaction. user's psychological factors aligned with the agent efficacy and correctness will be determining aspects. inspired by @xcite 's work, and by the computational model of curiosity of @xcite , we propose the introduction of dialog curiosities closely contextualized with certain flows of a conversation, to improve user satisfaction/engagement. in this context, our contributions are twofold: first, we propose a manually curated dataset of curiosities for the recipes and diy domains; second, we propose a robust method to naturally insert curiosities in dialogues foot_0 . an a/b test with over 1000 conversations, conducted with real alexa users, showed that the proposed approach achieves a relative rating improvement of 9.7%. ## curiosities dataset in this section, we explain the curiosities dataset creation process, that seeks to fill the existing gap with regards to dialog curiosities for task assistants in the recipes and diy domains. in particular, we considered the following principles: i) the curiosities' length matters significantly; ii) curiosities should be simple since dense and complex facts could have a negative impact on user engagement; and iii) the quality of each curiosity is more important than the number of curiosities. ## dialogues and contextual curiosities one of the objectives of our work is to incorporate curiosities into a conversational assistant's flow while users are being guided through a task in natural language @xcite . however, inserting the right curiosity in the right dialog turn is a non-trivial task. moreover, matching curiosities to a particular task with human effort would produce a high-quality reward, but, in practice, it is intractable due to the large amount of both tasks (> 100k) and curiosities (> 1k). to this end, we propose two distinct automatic approaches as described in the following sections. ## experimental results in this section, we detail the a/b testing setup and discuss the obtained results. ## conclusions in this paper, we presented a novel approach to introducing curiosities in conversations. specifically, we curated a dataset of curiosities in the recipes and diy domains and evaluated the impact of introducing these curiosities in real human-agent conversations in the alexa taskbot challenge. we assessed the impact of curiosities in a conversational task assistant setting, and the results allow us to conclude that introducing curiosities in a nonintrusive manner and in the context of the dialog can increase user engagement and improve their appreciation of the dialogue system. these findings have important implications for the design of conversational systems and can inform future research on incorporating curiosities in conversations to enhance the user experience. as future work, we will (1) investigate the use of generative models for creating factually grounded curiosities @xcite and compare them to manually curated curiosities; and (2) investigate methods that can contextualize the curiosities according to a graph of entities @xcite .