Hallucination of text ungrounded in the input is a well-known problem in
neural data-to-text generation. Many methods have been proposed to mitigate it,
but they typically require altering model architecture or collecting additional
data, and thus cannot be easily applied to an existing model. In this paper, we
explore a new way to mitigate hallucinations by combining the probabilistic
output of a generator language model (LM) with the output of a special "text
critic" classifier, which guides the generation by assessing the match between
the input data and the text generated so far. Our method does not need any
changes to the underlying LM's architecture or training procedure and can thus
be combined with any model and decoding operating on word probabilities. The
critic does not need any additional training data, using the base LM's training
data and synthetic negative examples. Our experimental results show that our
method improves over the baseline on the WebNLG and OpenDialKG benchmarks.

本文提出一种新方法，通过将生成器语言模型的概率输出与特殊的 “文本评论家” 分类器的输出结合起来，以评估输入数据与生成的文本之间的匹配程度，从而减轻神经数据到文本生成中的文字幻觉。实验证明，该方法对 WebNLG 和 OpenDialKG 基准具有改进效果。

批评驱动解码以减轻数据到文本生成中的幻觉

Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text  Generation

End-to-end neural data-to-text (D2T) generation has recently emerged as an
alternative to pipeline-based architectures. However, it has faced challenges
in generalizing to new domains and generating semantically consistent text. In
this work, we present DataTuner, a neural, end-to-end data-to-text generation
system that makes minimal assumptions about the data representation and the
target domain. We take a two-stage generation-reranking approach, combining a
fine-tuned language model with a semantic fidelity classifier. Each of our
components is learnt end-to-end without the need for dataset-specific
heuristics, entity delexicalization, or post-processing. We show that DataTuner
achieves state of the art results on the automated metrics across four major
D2T datasets (LDC2017T10, WebNLG, ViGGO, and Cleaned E2E), with a fluency
assessed by human annotators nearing or exceeding the human-written reference
texts. We further demonstrate that the model-based semantic fidelity scorer in
DataTuner is a better assessment tool compared to traditional, heuristic-based
measures. Our generated text has a significantly better semantic fidelity than
the state of the art across all four datasets

DataTuner 是一个神经网络端到端生成数据文本的系统，结合 fine-tuned 语言模型和语义保真分类器，仅需最少的数据特定启发式、实体去词法和后处理即可实现所有端到端学习，可以在多种数据集上取得最先进的结果并获得比传统启发式度量更好的语义保真度。

端到端神经数据文本生成：从数据到文本生成语义忠实性

Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation  with Semantic Fidelity

We follow the step-by-step approach to neural data-to-text generation we
proposed in Moryossef et al (2019), in which the generation process is divided
into a text-planning stage followed by a plan-realization stage. We suggest
four extensions to that framework: (1) we introduce a trainable neural planning
component that can generate effective plans several orders of magnitude faster
than the original planner; (2) we incorporate typing hints that improve the
model's ability to deal with unseen relations and entities; (3) we introduce a
verification-by-reranking stage that substantially improves the faithfulness of
the resulting texts; (4) we incorporate a simple but effective referring
expression generation module. These extensions result in a generation process
that is faster, more fluent, and more accurate.

本文从 Moryossef et al （2019）所提出的步骤式神经数据到文本生成方法出发，通过引入可训练的神经计划组件、打字提示、排序确认阶段和简单而有效的指代表达式生成模块的四个扩展，实现了一个更快，更流畅和更准确的生成过程。