Knowledge-enhanced pre-trained language models (KEPLMs) leverage relation
triples from knowledge graphs (KGs) and integrate these external data sources
into language models via self-supervised learning. Previous works treat
knowledge enhancement as two independent operations, i.e., knowledge injection
and knowledge integration. In this paper, we propose to learn
Knowledge-Enhanced language representations with Hierarchical Reinforcement
Learning (KEHRL), which jointly addresses the problems of detecting positions
for knowledge injection and integrating external knowledge into the model in
order to avoid injecting inaccurate or irrelevant knowledge. Specifically, a
high-level reinforcement learning (RL) agent utilizes both internal and prior
knowledge to iteratively detect essential positions in texts for knowledge
injection, which filters out less meaningful entities to avoid diverting the
knowledge learning direction. Once the entity positions are selected, a
relevant triple filtration module is triggered to perform low-level RL to
dynamically refine the triples associated with polysemic entities through
binary-valued actions. Experiments validate KEHRL's effectiveness in probing
factual knowledge and enhancing the model's performance on various natural
language understanding tasks.

本文提出了一种通过层次强化学习来学习知识增强语言表示的方法，该方法集成了知识图谱中的关系三元组，并通过自监督学习将这些外部数据源整合到语言模型中，以避免注入不准确或不相关的知识，并有效地提升了模型在各种自然语言理解任务上的性能。

KEHRL: 使用层次强化学习学习知识增强语言表征

KEHRL: Learning Knowledge-Enhanced Language Representations with  Hierarchical Reinforcement Learning

In this paper, we present RTSUM, an unsupervised summarization framework that
utilizes relation triples as the basic unit for summarization. Given an input
document, RTSUM first selects salient relation triples via multi-level salience
scoring and then generates a concise summary from the selected relation triples
by using a text-to-text language model. On the basis of RTSUM, we also develop
a web demo for an interpretable summarizing tool, providing fine-grained
interpretations with the output summary. With support for customization
options, our tool visualizes the salience for textual units at three distinct
levels: sentences, relation triples, and phrases. The codes,are publicly
available.

RTSUM 是一种无监督的摘要框架，利用关系三元组作为摘要的基本单位。在 RTSUM 的基础上，我们还开发了一个可解释的摘要工具的网络演示，提供了与输出摘要相关的精细解释。

RTSUM: 基于关系三元组的多层显著可视化的可解释摘要

RTSUM: Relation Triple-based Interpretable Summarization with  Multi-level Salience Visualization

We present a memory-augmented approach to condition an autoregressive
language model on a knowledge graph. We represent the graph as a collection of
relation triples and retrieve relevant relations for a given context to improve
text generation. Experiments on WikiText-103, WMT19, and enwik8 English
datasets demonstrate that our approach produces a better language model in
terms of perplexity and bits per character. We also show that relational memory
improves coherence, is complementary to token-based memory, and enables causal
interventions. Our model provides a simple yet effective way to combine an
autoregressive language model with a knowledge graph for a more coherent and
logical generation.

提出了一种基于知识图谱的记忆增强自回归语言模型，通过检索相关关系以改善文本生成，实验结果表明，这种方法在困惑度和字符比特方面产生了更好的语言模型，并且关系记忆有助于生成的连贯性，是词元记忆的补充。