The lack of high-quality data for content-grounded generation tasks has been identified as a major obstacle to advancing these tasks. To address this gap, we propose Genie, a novel method for automatically generating high-quality content-grounded data. It consists of three stages: (a) Content Preparation, (b) Generation: creating task-specific examples from the content (e.g., question-answer pairs or summaries). (c) Filtering mechanism aiming to ensure the quality and faithfulness of the generated data. We showcase this methodology by generating three large-scale synthetic data, making wishes, for Long-Form Question-Answering (LFQA), summarization, and information extraction. In a human evaluation, our generated data was found to be natural and of high quality. Furthermore, we compare models trained on our data with models trained on human-written data -- ELI5 and ASQA for LFQA and CNN-DailyMail for Summarization. We show that our models are on par with or outperforming models trained on human-generated data and consistently outperforming them in faithfulness. Finally, we applied our method to create LFQA data within the medical domain and compared a model trained on it with models trained on other domains.

Conversational agents, or chatbots, are widely used to provide customer care and other informational support. Currently, the development of chatbots using standard frameworks requires a lot of manual crafting by subject matter experts (SMEs). On the other hand, while learning-based approaches to dialog have made significant advancements, they require training with a large volume of dialog data, which chatbot developers typically do not have access to. To tackle these challenges, we introduce DOC2BOT, a system that supports the automated construction of chatbots by digesting various forms of documents such as business manuals, HowTos, and customer support pages that organizations own. In addition to that, DOC2BOT provides a user-friendly experience to SMEs, and to minimize their effort by supporting intuitive interactions and streamlining their workflow.

Jatin Ganhotra, Haggai Roitman, Doron Cohen, Nathaniel Mills, Chulaka Gunasekara, Yosi Mass, Sachindra Joshi, Luis Lastras, David Konopnicki. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.