Neural generation methods for task-oriented dialogue typically generate from
a meaning representation that is populated using a database of domain
information, such as a table of data describing a restaurant. While earlier
work focused solely on the semantic fidelity of outputs, recent work has
started to explore methods for controlling the style of the generated text
while simultaneously achieving semantic accuracy. Here we experiment with two
stylistic benchmark tasks, generating language that exhibits variation in
personality, and generating discourse contrast. We report a huge performance
improvement in both stylistic control and semantic accuracy over the state of
the art on both of these benchmarks. We test several different models and show
that putting stylistic conditioning in the decoder and eliminating the semantic
re-ranker used in earlier models results in more than 15 points higher BLEU for
Personality, with a reduction of semantic error to near zero. We also report an
improvement from .75 to .81 in controlling contrast and a reduction in semantic
error from 16% to 2%.

本研究旨在探索神经生成法如何同时实现语义准确度和文体控制，在两个文体基准任务中，通过在译码器中进行文体调节，消除先前模型中使用的语义再排序器，从而实现了语义误差降至接近零，并在 Personality 中实现了超过 15 个 BLEU 分数的巨大性能提升。在控制对比中还实现了从 0.75 到 0.81 的提升以及语义误差从 16% 降至 2%。

在 NLG 中最大化风格控制和语义准确性：个性变化和话语对比

Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality  Variation and Discourse Contrast

Recent neural models have shown significant progress on the problem of
generating short descriptive texts conditioned on a small number of database
records. In this work, we suggest a slightly more difficult data-to-text
generation task, and investigate how effective current approaches are on this
task. In particular, we introduce a new, large-scale corpus of data records
paired with descriptive documents, propose a series of extractive evaluation
methods for analyzing performance, and obtain baseline results using current
neural generation methods. Experiments show that these models produce fluent
text, but fail to convincingly approximate human-generated documents. Moreover,
even templated baselines exceed the performance of these neural models on some
metrics, though copy- and reconstruction-based extensions lead to noticeable
improvements.

研究采用新颖的数据生成文本的方式，对当前的文本生成方法进行了探究，并通过提出一系列抽取性评估方法和使用当前神经生成方法得到基线结果。实验结果表明，这些模型产生流畅的文本，但未能令人信服地近似人类生成的文档，甚至模板化的基线方法在某些指标上超过了这些神经模型，尽管复制和重构扩展会导致明显的改进。