Data-driven articles combine narrative text with visualizations to communicate insights but are time-consuming to produce, requiring both narrative composition and accurate text–chart linking. Existing automated linking methods struggle with accuracy, and automated narrative generation often misaligns with user intent and may contain hallucinations. We present VisCopilot, which leverages retrieval-augmented generation and large language models to provide fact-based, intent-aligned suggestions. As users write, the system supports narrative completion and text–chart linking in a single step, with a human-in-the-loop approach ensuring reliability. Technical evaluation on a benchmark dataset of data-driven articles authored by journalists confirmed the accuracy and quality of VisCopilot’s links. A user study further showed that VisCopilot outperforms baselines in usability, accuracy, and user confidence, with participants valuing its fact-based autocompletes for aligning suggestions with their writing intent. VisCopilot contributes: (1) an autocomplete-based design for assisting data-driven article authoring that balances efficiency, accuracy, and user autonomy; and (2) a reframing of text–chart link generation as a data-fact recommendation problem, enabling intent-aligned retrieval and systematic evaluation.

Despite recent success in neural task-oriented dialogue systems, developing such a real-world system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory network mechanisms. To alleviate the above problem, we propose , an end-to-end trainable text-to-SQL guided framework to learn a neural agent that interacts with KBs using the generated SQL queries. Specifically, the neural agent first learns to ask and confirm the customer’s intent during the multi-turn interactions, then dynamically determining when to ground the user constraints into executable SQL queries so as to fetch relevant information from KBs. With the help of our method, the agent can use less but more accurate fetched results to generate useful responses efficiently, instead of incorporating the entire KBs. We evaluate the proposed method on the AirDialogue dataset, a large corpus released by Google, containing the conversations of customers booking flight tickets from the agent. The experimental results show that significantly improves over previous work in terms of accuracy and the BLEU score, which demonstrates not only the ability to achieve the given task but also the good quality of the generated dialogues.