Related papers: Text Editing by Command

Text Editing by Command

URL: http://arxiv.org/abs/2010.12826v1
Date: Sat, 24 Oct 2020 08:00:30 GMT
Title: Text Editing by Command
Authors: Felix Faltings and Michel Galley and Gerold Hintz and Chris Brockett and Chris Quirk and Jianfeng Gao and Bill Dolan
Abstract summary: A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. We address this limitation with an interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. We show that our Interactive Editor, a transformer-based model trained on this dataset, outperforms baselines and obtains positive results in both automatic and human evaluations.
Score: 82.50904226312451
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. The one-shot setting is inadequate, however, when the constraints the user wishes to impose on the generated text are dynamic, especially when authoring longer documents. We address this limitation with an interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. To this end, we propose a novel text editing task, and introduce WikiDocEdits, a dataset of single-sentence edits crawled from Wikipedia. We show that our Interactive Editor, a transformer-based model trained on this dataset, outperforms baselines and obtains positive results in both automatic and human evaluations. We present empirical and qualitative analyses of this model's performance.

Related papers

WikiIns: A High-Quality Dataset for Controlled Text Editing by Natural Language Instruction [56.196512595940334]
We build and release WikiIns, a high-quality controlled text editing dataset with improved informativeness. With the high-quality annotated dataset, we propose automatic approaches to generate a large-scale silver'' training set.
arXiv Detail & Related papers (2023-10-08T04:46:39Z)
Copy Is All You Need [66.00852205068327]
We formulate text generation as progressively copying text segments from an existing text collection. Our approach achieves better generation quality according to both automatic and human evaluations. Our approach attains additional performance gains by simply scaling up to larger text collections.
arXiv Detail & Related papers (2023-07-13T05:03:26Z)
Text Revision by On-the-Fly Representation Optimization [76.11035270753757]
Current state-of-the-art methods formulate these tasks as sequence-to-sequence learning problems. We present an iterative in-place editing approach for text revision, which requires no parallel data. It achieves competitive and even better performance than state-of-the-art supervised methods on text simplification.
arXiv Detail & Related papers (2022-04-15T07:38:08Z)
A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publications [0.02578242050187029]
This paper presents two datasets comprised of artificially generated research content. In the first case, the content is completely generated by the GPT-2 model after a short prompt extracted from original papers. The partial or hybrid dataset is created by replacing several sentences of abstracts with sentences that are generated by the Arxiv-NLP model. We evaluate the quality of the datasets comparing the generated texts to aligned original texts using fluency metrics such as BLEU and ROUGE.
arXiv Detail & Related papers (2022-02-04T08:16:56Z)
Automatic Document Sketching: Generating Drafts from Analogous Texts [44.626645471195495]
We introduce a new task, document sketching, which involves generating entire draft documents for the writer to review and revise. These drafts are built from sets of documents that overlap in form - sharing large segments of potentially reusable text - while diverging in content. We investigate the application of weakly supervised methods, including use of a transformer-based mixture of experts, together with reinforcement learning.
arXiv Detail & Related papers (2021-06-14T06:46:06Z)
Data-to-Text Generation with Iterative Text Editing [3.42658286826597]
We present a novel approach to data-to-text generation based on iterative text editing. We first transform data items to text using trivial templates, and then we iteratively improve the resulting text by a neural model trained for the sentence fusion task. The output of the model is filtered by a simple and reranked with an off-the-shelf pre-trained language model.
arXiv Detail & Related papers (2020-11-03T13:32:38Z)
Fact-based Text Editing [11.115292572080131]
textscFactEditor edits a draft text by referring to given facts using a buffer, a stream, and a memory. textscFactEditor conducts inference faster than the encoder-decoder approach.
arXiv Detail & Related papers (2020-07-02T06:50:30Z)
POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training [93.79766670391618]
We present POINTER, a novel insertion-based approach for hard-constrained text generation. The proposed method operates by progressively inserting new tokens between existing tokens in a parallel manner. The resulting coarse-to-fine hierarchy makes the generation process intuitive and interpretable.
arXiv Detail & Related papers (2020-05-01T18:11:54Z)
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation [50.01708049531156]
We focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer. In detail, the input is a set of structured records and a reference text for describing another recordset. The output is a summary that accurately describes the partial content in the source recordset with the same writing style of the reference.
arXiv Detail & Related papers (2020-02-24T12:52:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.