Fact-based Text Editing

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 171–182 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 171 Fact-based Text Editing Hayate Iso†∗ Chao Qiao‡ Hang Li‡ †Nara Institute of Science and Technology ‡ByteDance AI Lab hyate.iso@gmail.com, {qiaochao, lihang.lh}@bytedance.com Abstract We propose a novel text editing task, referred to as fact-based text editing, in which the goal is to revise a given document to better de- scribe the facts in a knowledge base (e.g., sev- eral triples). The task is important in practice because reflecting the truth is a common re- quirement 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, respec- tively. Next, we propose a new neural network architecture for fact-based text editing, called FACTEDITOR, which edits a draft text by re- ferring 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 re- sults on the two datasets show that FACTE- DITOR outperforms the encoder-decoder ap- proach in terms of fidelity and fluency. The results also show that FACTEDITOR conducts inference faster than the encoder-decoder ap- proach. 1 Introduction Automatic editing of text by computer is an impor- tant application, which can help human writers to write better documents in terms of accuracy, flu- ency, etc. The task is easier and more practical than the automatic generation of texts from scratch and is attracting attention recently (Yang et al., 2017; Yin et al., 2019). In this paper, we consider a new and specific setting of it, referred to as fact-based text editing, in which a draft text and several facts (represented in triples) are given, and the system ∗ The work was done when Hayate Iso was a research intern at ByteDance AI Lab. Set of triples {(Baymax, creator, Douncan Rouleau), (Douncan Rouleau, nationality, American), (Baymax, creator, Steven T. Seagle), (Steven T. Seagle, nationality, American), (Baymax, series, Big Hero 6), (Big Hero 6, starring, Scott Adsit)} Draft text Baymax was created by Duncan Rouleau, a winner of Eagle Award. Baymax is a character in Big Hero 6 . Revised text Baymax was created by American creators Duncan Rouleau and Steven T. Seagle . Baymax is a character in Big Hero 6 which stars Scott Adsit . Table 1: Example of fact-based text editing. Facts are represented in triples. The facts in green appear in both draft text and triples. The facts in orange are present in the draft text, but absent from the triples. The facts in blue do not appear in the draft text, but in the triples. The task of fact-based text editing is to edit the draft text on the basis of the …

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