This paper introduces a novel neuro-symbolic architecture for relation
classification (RC) that combines rule-based methods with contemporary deep
learning techniques. This approach capitalizes on the strengths of both
paradigms: the adaptability of rule-based systems and the generalization power
of neural networks. Our architecture consists of two components: a declarative
rule-based model for transparent classification and a neural component to
enhance rule generalizability through semantic text matching. Notably, our
semantic matcher is trained in an unsupervised domain-agnostic way, solely with
synthetic data. Further, these components are loosely coupled, allowing for
rule modifications without retraining the semantic matcher. In our evaluation,
we focused on two few-shot relation classification datasets: Few-Shot TACRED
and a Few-Shot version of NYT29. We show that our proposed method outperforms
previous state-of-the-art models in three out of four settings, despite not
seeing any human-annotated training data. Further, we show that our approach
remains modular and pliable, i.e., the corresponding rules can be locally
modified to improve the overall model. Human interventions to the rules for the
TACRED relation \texttt{org:parents} boost the performance on that relation by
as much as 26\% relative improvement, without negatively impacting the other
relations, and without retraining the semantic matching component.

介绍了一种新的神经符号结构用于关系分类，结合了基于规则的方法和现代深度学习技术，通过语义文本匹配增强规则泛化能力，该方法在两个 few-shot 关系分类数据集上的评估中，表明在四个设置中有三个的性能优于之前的最先进模型。

最佳的两全之策：一种灵活且具有泛化能力的神经符号方法用于关系分类

Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach  for Relation Classification

This paper creates a paradigm shift with regard to the way we build neural
extractive summarization systems. Instead of following the commonly used
framework of extracting sentences individually and modeling the relationship
between sentences, we formulate the extractive summarization task as a semantic
text matching problem, in which a source document and candidate summaries will
be (extracted from the original text) matched in a semantic space. Notably,
this paradigm shift to semantic matching framework is well-grounded in our
comprehensive analysis of the inherent gap between sentence-level and
summary-level extractors based on the property of the dataset.
Besides, even instantiating the framework with a simple form of a matching
model, we have driven the state-of-the-art extractive result on CNN/DailyMail
to a new level (44.41 in ROUGE-1). Experiments on the other five datasets also
show the effectiveness of the matching framework. We believe the power of this
matching-based summarization framework has not been fully exploited. To
encourage more instantiations in the future, we have released our codes,
processed dataset, as well as generated summaries in
this https URL

通过语义文本匹配任务范式，本论文提出了一种新的神经抽取式摘要系统构建方法。经过与原有抽取式摘要方法数据集的实验对比，该方法取得了更好的抽取结果，并通过对 CNN / DailyMail 数据集的实验，创造了 44.41 的新高水平。我们相信，这种基于匹配的摘要框架的潜力还未完全被利用。