Stylized Dialog Response Generation (2023)

Abstract: My primary research focus lies in the domain of Text Style Transfer (TST), a fascinating area within Natural Language Processing (NLP). TST involves the transfor- mation of text into a desired style while approximately preserving its underlying content. In my research, I am also driven by the goal of incorporating TST techniques into NLP systems, particularly within the realm of dia- logue systems. I am intrigued by the concept of Stylized Dialog Response Generation, which aims to enhance the versatility and adaptability of dialog systems in generat- ing text responses with specific style attributes. By ad- vancing our understanding of TST and its integration into dialogue systems, my research seeks to contribute to the broader field of human-computer interaction. Through the development of robust and versatile dialogue systems with enhanced style transfer capabilities, we can facili- tate more engaging and personalized conversational experiences.

## research interests my primary research focus lies in the domain of text style transfer (tst), a fascinating area within natural language processing (nlp). tst involves the transformation of text into a desired style while approximately preserving its underlying content. in my research, i am also driven by the goal of incorporating tst techniques into nlp systems, particularly within the realm of dialogue systems. i am intrigued by the concept of stylized dialog response generation, which aims to enhance the versatility and adaptability of dialog systems in generating text responses with specific style attributes. by advancing our understanding of tst and its integration into dialogue systems, my research seeks to contribute to the broader field of human-computer interaction. through the development of robust and versatile dialogue systems with enhanced style transfer capabilities, we can facilitate more engaging and personalized conversational experiences. ## spoken dialogue system (sds) research in the next 5 to 10 years, the field of dialogue research will witness significant advancements. young researchers have the opportunity to contribute to transformative developments in spoken dialogue systems (sds). the convergence of academia and industry will narrow the gap between theoretical advancements and practical applications. this collaboration will lead to more robust and adaptable sds architectures, enabling non-experts to create virtual conversational agents and collaborative assistants easily. key questions to address include leveraging language models for practical task-oriented dialogue systems, incorporating cognitive modeling to enhance goal-driven behavior, and focusing on user-centricity and extreme personalization. there are differences between sds research in academia and industry, with academia emphasizing fully automated learning and interpretability, while industry research gradually incorporates neural components into hand-coded systems. sds will be widely used in various scenarios, including voice assistants in everyday devices, specialized applications like car assistants and healthcare, and "ai for good" initiatives for accessibility and inclusivity. in summary, the future of sds research lies in the convergence of academia and industry, the development of user-centric and personalized dialogue systems, and collaboration between interdisciplinary researchers. ## suggested topics for discussion as we delve into the exciting realm of spoken dialogue systems (sds) research, we propose three thoughtprovoking topics for discussion during the event. these topics not only align with our expertise but also resonate with the interests and relevance to the young research community. stylistic expressiveness in dialogue systems: one area of focus is exploring the potential of text style transfer (tst) in stylized dialog response generation. discussions can revolve around advancements in generating stylistically expressive responses, including but not limited to polite dialog generation, personalized dialog generation, and other forms of stylized dialog response generation. sharing best practices, challenges, and novel techniques to achieve high-quality and contextually appropriate stylized responses would enrich our understanding of how to enhance the naturalness and user satisfaction in sds interactions. evaluation metrics for stylized dialog systems: evaluation plays a crucial role in assessing the effectiveness and performance of sds, particularly in the context of stylized dialog response generation. engaging in discussions about the development of evaluation methodologies, metrics, and benchmarks specific to stylistic qualities would greatly benefit the research community. by addressing challenges such as subjective assessment, crosssystem comparison, and capturing the nuances of style, we can establish standardized evaluation practices that facilitate fair and comprehensive evaluations of different stylized dialog systems.