Neural Table-to-Text models tend to hallucinate, producing texts that contain
factual errors. We investigate whether such errors in the output can be traced
back to problems with the input. We manually annotated 1,837 texts generated by
multiple models in the politics domain of the ToTTo dataset. We identify the
input problems that are responsible for many output errors and show that fixing
these inputs reduces factual errors by between 52% and 76% (depending on the
model). In addition, we observe that models struggle in processing tabular
inputs that are structured in a non-standard way, particularly when the input
lacks distinct row and column values or when the column headers are not
correctly mapped to corresponding values.

研究表明，神经表格文本转换模型在输出时往往会产生错误，这些错误可以追溯到输入问题，通过对输入进行修正可以将错误率降低 52% 至 76%（取决于模型）。此外，发现模型在处理非标准结构的表格输入时存在困难，特别是当输入缺乏明确的行和列值，或者列标题没有正确映射到相应的值时。

解决 ToTTo 中输入问题，提高神经表格到文本输出的事实准确性

Improving Factual Accuracy of Neural Table-to-Text Output by Addressing  Input Problems in ToTTo

In this work we study user controlled table-to-text generation where users
explore the content in a table by selecting cells and reading a natural
language description thereof automatically produce by a natural language
generator. Such generation models usually learn from carefully selected cell
combinations (clean cell selections); however, in practice users may select
unexpected, redundant, or incoherent cell combinations (noisy cell selections).
In experiments, we find that models perform well on test sets coming from the
same distribution as the train data but their performance drops when evaluated
on realistic noisy user inputs. We propose a fine-tuning regime with additional
user-simulated noisy cell selections. Models fine-tuned with the proposed
regime gain 4.85 BLEU points on user noisy test cases and 1.4 on clean test
cases; and achieve comparable state-of-the-art performance on the ToTTo
dataset.

本研究探讨用户控制的表格转文本生成，针对嘈杂、冗余、不连贯的单元选择提出了一种微调策略，使生成模型的性能可以在用户噪声输入上得到提高，同时在 ToTTo 数据集上达到与现有最先进模型相当的性能水平。

提高用户控制的表格到文本生成鲁棒性

Improving User Controlled Table-To-Text Generation Robustness

Data-to-text (D2T) generation is the task of generating texts from structured
inputs. We observed that when the same target sentence was repeated twice,
Transformer (T5) based model generates an output made up of asymmetric
sentences from structured inputs. In other words, these sentences were
different in length and quality. We call this phenomenon "Asymmetric
Generation" and we exploit this in D2T generation. Once asymmetric sentences
are generated, we add the first part of the output with a no-repeated-target.
As this goes through progressive edit (ProEdit), the recall increases. Hence,
this method better covers structured inputs than before editing. ProEdit is a
simple but effective way to improve performance in D2T generation and it
achieves the new stateof-the-art result on the ToTTo dataset

该研究通过利用 Transformer 模型的 'Asymmetric Generation' 现象并结合 ProEdit 方法，提出了一种改进的 Data-to-text generation 方法，有效提高了在 ToTTo 数据集上的性能表现。