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

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the
performance of various downstream NLP tasks by injecting knowledge facts from
large-scale Knowledge Graphs (KGs). However, existing methods for pre-training
KEPLMs with relational triples are difficult to be adapted to close domains due
to the lack of sufficient domain graph semantics. In this paper, we propose a
Knowledge-enhanced lANGuAge Representation learning framework for various
clOsed dOmains (KANGAROO) via capturing the implicit graph structure among the
entities. Specifically, since the entity coverage rates of closed-domain KGs
can be relatively low and may exhibit the global sparsity phenomenon for
knowledge injection, we consider not only the shallow relational
representations of triples but also the hyperbolic embeddings of deep
hierarchical entity-class structures for effective knowledge fusion.Moreover,
as two closed-domain entities under the same entity-class often have locally
dense neighbor subgraphs counted by max point biconnected component, we further
propose a data augmentation strategy based on contrastive learning over
subgraphs to construct hard negative samples of higher quality. It makes the
underlying KELPMs better distinguish the semantics of these neighboring
entities to further complement the global semantic sparsity. In the
experiments, we evaluate KANGAROO over various knowledge-aware and general NLP
tasks in both full and few-shot learning settings, outperforming various KEPLM
training paradigms performance in closed-domains significantly.

Knowledge-Enhanced Pre-trained Language Models improve downstream NLP tasks in closed domains by injecting knowledge facts from Knowledge Graphs using the proposed KANGAROO framework that captures implicit graph structure and employs data augmentation for better performance.

学习领域自然语言理解中的知识增强上下文语言表示

Learning Knowledge-Enhanced Contextual Language Representations for  Domain Natural Language Understanding

Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve
context-aware representations via learning from structured relations in
knowledge graphs, and/or linguistic knowledge from syntactic or dependency
analysis. Unlike English, there is a lack of high-performing open-source
Chinese KEPLMs in the natural language processing (NLP) community to support
various language understanding applications. In this paper, we revisit and
advance the development of Chinese natural language understanding with a series
of novel Chinese KEPLMs released in various parameter sizes, namely CKBERT
(Chinese knowledge-enhanced BERT).Specifically, both relational and linguistic
knowledge is effectively injected into CKBERT based on two novel pre-training
tasks, i.e., linguistic-aware masked language modeling and contrastive
multi-hop relation modeling. Based on the above two pre-training paradigms and
our in-house implemented TorchAccelerator, we have pre-trained base (110M),
large (345M) and huge (1.3B) versions of CKBERT efficiently on GPU clusters.
Experiments demonstrate that CKBERT outperforms strong baselines for Chinese
over various benchmark NLP tasks and in terms of different model sizes.

本文提出了中文知识增强的预训练语言模型 CKBERT，其中包括语言感知的掩模语言模型和对比多跳关系模型等预训练任务。通过实验对比发现，CKBERT 在各种基准 NLP 任务和不同模型尺寸方面均优于强负载。