We present a novel approach to data-to-text generation based on iterative
text editing. Our approach maximizes the completeness and semantic accuracy of
the output text while leveraging the abilities of recent pre-trained models for
text editing (LaserTagger) and language modeling (GPT-2) to improve the text
fluency. To this end, 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 heuristic and reranked with an off-the-shelf pre-trained language model.
We evaluate our approach on two major data-to-text datasets (WebNLG, Cleaned
E2E) and analyze its caveats and benefits. Furthermore, we show that our
formulation of data-to-text generation opens up the possibility for zero-shot
domain adaptation using a general-domain dataset for sentence fusion.

本研究提出了一种基于迭代文本编辑的数据到文本生成新方法，使用两个先前训练模型 LaserTagger 和 GPT-2，并通过简单的启发式筛选和已训练语言模型对输出进行筛选和重新排序。

迭代文本编辑的数据生成

Data-to-Text Generation with Iterative Text Editing

We propose LaserTagger - a sequence tagging approach that casts text
generation as a text editing task. Target texts are reconstructed from the
inputs using three main edit operations: keeping a token, deleting it, and
adding a phrase before the token. To predict the edit operations, we propose a
novel model, which combines a BERT encoder with an autoregressive Transformer
decoder. This approach is evaluated on English text on four tasks: sentence
fusion, sentence splitting, abstractive summarization, and grammar correction.
LaserTagger achieves new state-of-the-art results on three of these tasks,
performs comparably to a set of strong seq2seq baselines with a large number of
training examples, and outperforms them when the number of examples is limited.
Furthermore, we show that at inference time tagging can be more than two orders
of magnitude faster than comparable seq2seq models, making it more attractive
for running in a live environment.

LaserTagger 是一种将文本生成转换为文本编辑任务的序列标注方法，并通过使用 BERT 编码器和自回归 Transformer 解码器的新型模型来预测编辑操作。在四个任务中测试表明，LaserTagger 在三个任务上取得了新的最优结果，并在训练示例数量有限的情况下优于 seq2seq 模型。同时，LaserTagger 在推理时的标注速度比相似的 seq2seq 模型快两个数量级。