Towards More Natural Dialogues: Integrating Open-Domain Dialogue Skills into Task-Oriented Agents (2023)

Abstract: Position paper on the intersection between chitchat and task-oriented dialogues (TODs), with a focus on integrating capabilities typically associated with chitchat systems into task-oriented agents.

## research interests my research interests lie at the intersection between chitchat and task-oriented dialogues (tods), with a specific focus on integrating capabilities typically associated with chitchat systems into task-oriented agents. in the sds literature, these two modes of communication are commonly depicted with a clear contrast. on the one hand, chitchat dialogues are characterized as open-domain and usually involve a wide range of topics; chitchat agents are expected to embody all the qualities of an ideal conversationalist and be empathetic, engaging, knowledgeable, and well-behaved. on the other hand, tods are closed-domain and rely on specific databases and ontologies; task agents are designed to be efficient and effective tools. additionally, the goals of these respective systems are often presented as opposites: a lengthy conversation with a chitchat agent is generally perceived as successful, indicating user engagement and interest, whereas a prolonged conversation with a task-oriented agent is typically considered unsuccessful or suboptimal, suggesting that the user's needs were not met or were met with difficulty. however, these distinctions are not as clear-cut when it comes to human communication. most language is not purely transactional foot_0 or interactional foot_1 but a mix of both. in fact, exchanges are generally better described as primarily transactional or interactional @xcite . in the context of task-oriented dialogues, a system that lacks the ability to exhibit remorse when making errors, display empathy when a user's favorite restaurant is unavailable, or address additional context such as having dinner with one's boss vs. with a friend hampers humansystem collaboration. ## spoken dialogue system (sds) research predicting the state of sds in the next 5 or even 10 years is challenging due to the rapid evolution of the field. however, it is clear that sds and text-based dialogue systems are here to stay, offering a convenient means of interacting with machines for non-specialist users, in turn generating increased interest from actors in industry. this enthusiasm drives the direction of sds towards more reliable systems (particularly in executing tasks like booking tickets or restaurant reservations) capable of providing accurate, relevant and non-hallucinated information. another promising trend is the growing emphasis on personalization and adaptation of these agents to individual users' preferences, needs, and communication styles. leveraging the capabilities of llms for sds presents an exciting opportunity for young researchers in the coming years. understanding where llms fit in relation to tods is an important topic. it raises questions such as: can these models be employed in an end-to-end manner? should they be utilized more prominently in specific components such as natural language understanding or natural language generation? can they generate training data for early-stage prototyping while awaiting the collection of real-world data? given their ability to follow instructions and generate coherent text, can they serve as user simulators that account for character traits/personas as well as task-oriented goals ? while llms showcase powerful language capabilities, ensuring they provide precise, factual, and reliable information within narrow domains, as found in tods, poses a non-trivial challenge. in this regard, research focusing on implementing safeguard mechanisms and constraints for these models needs to be carried out, especially if they are to be employed at scale in sensitive environments.