Dialog Inpainting: Turning Documents into Dialogs

By Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Y. Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu. 2022

77 citations recorded by the source corpus.

Many important questions (e.g."How to eat healthier?") require conversation to establish context and explore in depth. However, conversational question answering (ConvQA) systems have long been stymied by scarce training data that is expensive to collect. To address this problem, we propose a new technique for synthetically generating diverse and high-quality dialog data: dialog inpainting. Our approach takes the text of any document and transforms it into a two-person dialog between the writer and an imagined reader: we treat sentences from the article as utterances spoken by the writer, and then use a dialog inpainter to predict what the imagined reader asked or said in between each of the writer's utterances. By applying this approach to passages from Wikipedia and the web, we produce WikiDialog and WebDialog, two datasets totalling 19 million diverse information-seeking dialogs -- 1,000x larger than the largest existing ConvQA dataset. Furthermore, human raters judge the answer adequacy and conversationality of WikiDialog to be as good or better than existing manually-collected datasets. Using our inpainted data to pre-train ConvQA retrieval systems, we significantly advance state-of-the-art across three benchmarks (QReCC, OR-QuAC, TREC CAsT) yielding up to 40% relative gains on standard evaluation metrics.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Scaling Instruction-Finetuned Language Models (2022, background): “(2021) and 26 new tasks that we added in this work, including dialog data (Byrne et al., 2019; Anantha et al., 2021; Dai et al., 2022) and program synthesis data (Yasunaga and Liang, 2020; Li et al.”
PEER: A Collaborative Language Model (2022, methodology): “Similar to how we use models to turn plain texts into sequences of edits, Dai et al. (2022) turn documents into dialogue sequences.”
LongForm: Effective Instruction Tuning with Reverse Instructions (2023, neutral): “The dataset is split 23,652/2,042/2,045 into training/validation/test.”
Large Language Models for Information Retrieval: A Survey (2023, unknown): “However, the limited ability of semantic understanding and comprehension of user search intent limits their performance in capturing the full scope of user intent.”
RARR: Researching and Revising What Language Models Say, Using Language Models (2022, methodology): “…be a useful tool for many tasks, such as open domain information extraction (Lewis et al., 2021; Chen et al., 2022), summarization (Weiss et al., 2021; Narayan et al., 2022), document retrieval (Ma et al., 2021; Hui et al., 2022), factchecking (Honovich et al., 2021) and dialog (Dai et al., 2022).”
Attributed Text Generation via Post-hoc Research and Revision (2022, methodology): “…be a useful tool for many tasks, such as open domain information extraction (Lewis et al., 2021; Chen et al., 2022), summarization (Weiss et al., 2021; Narayan et al., 2022), document retrieval (Ma et al., 2021; Hui et al., 2022), fact-checking (Honovich et al., 2021) and dialog (Dai et al., 2022).”