~hackernoon | Bookmarks (5)

Explore the intricacies of the Integrated Error Taxonomy by Higashinaka et al. (2021), unraveling language-generation challenges across utterance, response, context, and society levels. Understand distinctions between form and content violations, gaining insights into errors in system utterances and responses.

Explore the possibilities and challenges in AI dialog learning, unraveling insights into errors and user responses in various datasets. Discover the potential for extending datasets to facilitate learning from free-text human feedback, emphasizing the richness of human-bot dialogs. The article proposes new taxonomies and highlights the positive impact of incorporating errors and user responses in response generation. As the journey concludes, acknowledge the limitations...

Discover the scarcity of free-text human feedback annotations in existing dialog datasets and witness a paradigm shift in error taxonomies within conversational AI. This article examines six prominent datasets, critiques current taxonomies, and introduces a more versatile error classification system. Uncover the nuances of user responses and explore the boundaries of error taxonomy in AI dialog systems.

Delve into the world of dialog systems as researchers investigate the types and frequency of free-text human feedback in popular datasets. Explore the potential of synthetic dialog generation to augment existing data, uncovering insights into error types and user responses. Learn about new taxonomies derived from the analysis and their impact on response generation in cutting-edge language models like GPT-2, LLAMA, and FlanT5.