Exploring the Synergy of Deep Learning and Anthropomorphism in Multimodal Dialogue Systems (2023)

Abstract: This position paper is an overview of author’s main research interests and work considering deep learning techniques in audio classification, sign languages, and multimodality in dialogue systems. Author also shares her opinion on current and future research considering dialogue agents, and suggests topics for discussion panels.

## spoken dialogue system (sds) research currently, the biggest issue with dialogue systems is hallucination -they generate grammatically correct texts that are contentually incorrect. research should therefore focus on creating chatbots that are able to back up their statements with relevant sources. to achieve this, young researchers should focus first and foremost on developing good quality data and extracting valuable information. this is the basis for obtaining substantively correct and satisfactory results. the accuracy of information is also important for users. they should be able to get reliable answers with prompts that do not require specialized knowledge. in addition, multimodality, such as 3d models or humanlike robots, should be used to provide a more realistic experience. nowadays, academic research is more focused on lowresource issues. these are matters that require attention but do not generate income. companies, on the other hand, prefer to focus on more profitable ventures. finding balance between both approaches is crucial to the development of dialogue systems. it is worth pursuing research on dialogue systems, as they will be used even more extensively in the futureespecially in the form of virtual assistants, helpline assistance, or emergency calls.