Fact-based Text Editing
arXiv:2007.00916 · doi:10.18653/v1/2020.acl-main.17
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 address the problem would be to employ an encoder-decoder model. Our experimental results on the two datasets show that \textsc{FactEditor} outperforms the encoder-decoder approach in terms of fidelity and fluency. The results also show that \textsc{FactEditor} conducts inference faster than the encoder-decoder approach.
arXiv commentACL 2020
References in corpus (13)
- Neural Machine Translation by Jointly Learning to Align and Translate
- Sequence to Sequence Learning with Neural Networks
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- On the Convergence of Adam and Beyond
- Building Natural-Language Generation Systems
- Style Transfer from Non-Parallel Text by Cross-Alignment
- Transition-Based Dependency Parsing with Stack Long Short-Term Memory
- Neural Programmer-Interpreters
- Automated Postediting of Documents
- A Retrieve-and-Edit Framework for Predicting Structured Outputs
- Learning to Represent Edits
- Learning to Select, Track, and Generate for Data-to-Text
- Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data