A system created by MIT researchers could be used to automatically update factual inconsistencies in Wikipedia articles, reducing time and effort spent by human editors who now do the task manually.
In a paper being presented at the AAAI Conference on Artificial Intelligence, the researchers describe a text-generating system that pinpoints and replaces specific information in relevant Wikipedia sentences, while keeping the language similar to how humans write and edit.
The idea is that humans would type into an interface an unstructured sentence with updated information, without needing to worry about style or grammar. The system would then search Wikipedia, locate the appropriate page and outdated sentence, and rewrite it in a humanlike fashion. In the future, the researchers say, there’s potential to build a fully automated system that identifies and uses the latest information from around the web to produce rewritten sentences in corresponding Wikipedia articles that reflect updated information.
“There are so many updates constantly needed to Wikipedia articles. It would be beneficial to automatically modify exact portions of the articles, with little to no human intervention,” says Darsh Shah, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and one of the lead authors. “Instead of hundreds of people working on modifying each Wikipedia article, then you’ll only need a few, because the model is helping or doing it automatically. That offers dramatic improvements in efficiency.”
Many other bots exist that make automatic Wikipedia edits. Typically, those work on mitigating vandalism or dropping some narrowly defined information into predefined templates, Shah says. The researchers’ model, he says, solves a harder artificial intelligence problem: Given a new piece of unstructured information, the model automatically modifies the sentence in a humanlike fashion. “The other [bot] tasks are more rule-based, while this is a task requiring reasoning over contradictory parts in two sentences and generating a coherent piece of text,” he says.
The system can be used for other text-generating applications as well, says co-lead author and CSAIL graduate student Tal Schuster. In their paper, the researchers also used it to automatically synthesize sentences in a popular fact-checking dataset that helped reduce bias, without manually collecting additional data. “This way, the performance improves for automatic fact-verification models that train on the dataset for, say, fake news detection,” Schuster says.
See Also: The Conference Paper the Article Linked Above Discusses is Available via arXiv: “Automatic Fact-Guided Sentence Modification”
10 pages; PDF.