Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?

arXiv:2307.07047v2 Announce Type: replace
Abstract: The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fact that most data is protected from public release for privacy reasons. Problem-solving human dialogues in real applications can be much more complex than existing Wizard-of-Oz collections, preventing successful domain transfer. To support information extraction (IE) for a private call center dataset, we introduce a human-in-the-loop dialogue generation framework capable of synthesizing …

abstract application applications arxiv capabilities collaborative cs.cl data dialogue explore extraction human information information extraction language language models opportunities privacy problem-solving public release type