Controlled text generation (CTG) seeks to guide large language model (LLM)
output to produce text that conforms to desired criteria. The current study
presents a novel CTG algorithm that enforces adherence toward specific
rhetorical relations in an LLM sentence-completion context by a parser-driven
decoding scheme that requires no model fine-tuning. The method is validated
both with automatic and human evaluation. The code is accessible on GitHub.

该研究提出了一种新颖的控制文本生成算法，通过解析驱动的解码方案，在大型语言模型的句子填充上下文中强制执行特定修辞关系，无需模型微调。该方法经过了自动和人工评估的验证。

基于解析驱动的修辞控制方法的语言模型句子补全

Language Model Sentence Completion with a Parser-Driven Rhetorical  Control Method

We present an approach for recursively splitting and rephrasing complex
English sentences into a novel semantic hierarchy of simplified sentences, with
each of them presenting a more regular structure that may facilitate a wide
variety of artificial intelligence tasks, such as machine translation (MT) or
information extraction (IE). Using a set of hand-crafted transformation rules,
input sentences are recursively transformed into a two-layered hierarchical
representation in the form of core sentences and accompanying contexts that are
linked via rhetorical relations. In this way, the semantic relationship of the
decomposed constituents is preserved in the output, maintaining its
interpretability for downstream applications. Both a thorough manual analysis
and automatic evaluation across three datasets from two different domains
demonstrate that the proposed syntactic simplification approach outperforms the
state of the art in structural text simplification. Moreover, an extrinsic
evaluation shows that when applying our framework as a preprocessing step the
performance of state-of-the-art Open IE systems can be improved by up to 346%
in precision and 52% in recall. To enable reproducible research, all code is
provided online.

该研究提出了一种递归分割和重述复杂英语句子的方法，生成简化句子的新语义层级结构，可用于机器翻译或信息提取等人工智能任务，手动和自动评估都表明该方法在结构文本简化方面表现优于现有技术，并用于近似 346% 精度和 52% 召回率地改良现有句子提取系统的性能。

将复杂句子转换为语义层级

Transforming Complex Sentences into a Semantic Hierarchy

Typically, every part in most coherent text has some plausible reason for its
presence, some function that it performs to the overall semantics of the text.
Rhetorical relations, e.g. contrast, cause, explanation, describe how the parts
of a text are linked to each other. Knowledge about this socalled discourse
structure has been applied successfully to several natural language processing
tasks. This work studies the use of rhetorical relations for Information
Retrieval (IR): Is there a correlation between certain rhetorical relations and
retrieval performance? Can knowledge about a document's rhetorical relations be
useful to IR? We present a language model modification that considers
rhetorical relations when estimating the relevance of a document to a query.
Empirical evaluation of different versions of our model on TREC settings shows
that certain rhetorical relations can benefit retrieval effectiveness notably
(> 10% in mean average precision over a state-of-the-art baseline).

通过研究修辞关系在信息检索中的应用，本文提出了一种基于语言模型修辞关系的改进方法，实证评估结果显示，使用特定的修辞关系可以显著提高检索有效性。