TL;DR
This paper introduces fact-based text editing, a new task for revising texts to accurately reflect knowledge base facts, along with datasets and a novel neural model called FactEditor that outperforms traditional encoder-decoder methods.
Contribution
It presents the first datasets for fact-based text editing, a new neural architecture FactEditor, and demonstrates its superior performance and speed over encoder-decoder models.
Findings
FactEditor outperforms encoder-decoder models in fidelity and fluency.
FactEditor is faster during inference.
New datasets with 233k and 37k instances were created for research.
Abstract
We propose a novel text editing task, referred to as \textit{fact-based text editing}, in which the goal is to revise a given document to better describe the facts in a knowledge base (e.g., several triples). The task is important in practice because reflecting the truth is a common requirement in text editing. First, we propose a method for automatically generating a dataset for research on fact-based text editing, where each instance consists of a draft text, a revised text, and several facts represented in triples. We apply the method into two public table-to-text datasets, obtaining two new datasets consisting of 233k and 37k instances, respectively. Next, we propose a new neural network architecture for fact-based text editing, called \textsc{FactEditor}, which edits a draft text by referring to given facts using a buffer, a stream, and a memory. A straightforward approach to…
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