Joint entity and relation extraction is the fundamental task of information
extraction, consisting of two subtasks: named entity recognition and relation
extraction. Most existing joint extraction methods suffer from issues of
feature confusion or inadequate interaction between two subtasks. In this work,
we propose a Co-Attention network for joint entity and Relation Extraction
(CARE). Our approach involves learning separate representations for each
subtask, aiming to avoid feature overlap. At the core of our approach is the
co-attention module that captures two-way interaction between two subtasks,
allowing the model to leverage entity information for relation prediction and
vice versa, thus promoting mutual enhancement. Extensive experiments on three
joint entity-relation extraction benchmark datasets (NYT, WebNLG and SciERC)
show that our proposed model achieves superior performance, surpassing existing
baseline models.

我们提出了一种名为 CARE 的 Co-Attention 网络模型，可以实现联合实体和关系的抽取任务，通过学习各个子任务的独立表示以避免特征重叠，并通过双向交互来提升模型性能，实验结果表明我们的模型在三个基准数据集上表现优秀。

CARE：用于联合实体和关系抽取的共同注意力网络

CARE: Co-Attention Network for Joint Entity and Relation Extraction

As a broad and major category in machine reading comprehension (MRC), the
generalized goal of discriminative MRC is answer prediction from the given
materials. However, the focuses of various discriminative MRC tasks may be
diverse enough: multi-choice MRC requires model to highlight and integrate all
potential critical evidence globally; while extractive MRC focuses on higher
local boundary preciseness for answer extraction. Among previous works, there
lacks a unified design with pertinence for the overall discriminative MRC
tasks. To fill in above gap, we propose a lightweight POS-Enhanced Iterative
Co-Attention Network (POI-Net) as the first attempt of unified modeling with
pertinence, to handle diverse discriminative MRC tasks synchronously. Nearly
without introducing more parameters, our lite unified design brings model
significant improvement with both encoder and decoder components. The
evaluation results on four discriminative MRC benchmarks consistently indicate
the general effectiveness and applicability of our model, and the code is
available at this https URL.

提出了轻量级的词性增强迭代协同网络（POI-Net），作为整体辨别性机器阅读理解的第一次尝试，旨在统一处理各种辨别性机器阅读理解任务，结果表明这种设计能够在不引入更多参数的情况下，有效地提高了模型在四个辨别性机器阅读理解基准测试上的性能。

轻量级统一建模用于判别式阅读理解

Lite Unified Modeling for Discriminative Reading Comprehension

Entity alignment (EA) is to discover equivalent entities in knowledge graphs
(KGs), which bridges heterogeneous sources of information and facilitates the
integration of knowledge. Existing EA solutions mainly rely on structural
information to align entities, typically through KG embedding. Nonetheless, in
real-life KGs, only a few entities are densely connected to others, and the
rest majority possess rather sparse neighborhood structure. We refer to the
latter as long-tail entities, and observe that such phenomenon arguably limits
the use of structural information for EA. To mitigate the issue, we revisit and
investigate into the conventional EA pipeline in pursuit of elegant
performance. For pre-alignment, we propose to amplify long-tail entities, which
are of relatively weak structural information, with entity name information
that is generally available (but overlooked) in the form of concatenated power
mean word embeddings. For alignment, under a novel complementary framework of
consolidating structural and name signals, we identify entity's degree as
important guidance to effectively fuse two different sources of information. To
this end, a degree-aware co-attention network is conceived, which dynamically
adjusts the significance of features in a degree-aware manner. For
post-alignment, we propose to complement original KGs with facts from their
counterparts by using confident EA results as anchors via iterative training.
Comprehensive experimental evaluations validate the superiority of our proposed
techniques.

通过使用实体名称信息和度量作为重要的指导信息，提出了 degree-aware co-attention 网络来识别结构和名称信号的新型互补框架，以有效地融合两种不同的信息源，然后使用有信心的 EA 结果作为锚点通过迭代训练来补充原始 KG 的事实。