Semantic matching is a mainstream paradigm of zero-shot relation extraction,
which matches a given input with a corresponding label description. The
entities in the input should exactly match their hypernyms in the description,
while the irrelevant contexts should be ignored when matching. However, general
matching methods lack explicit modeling of the above matching pattern. In this
work, we propose a fine-grained semantic matching method tailored for zero-shot
relation extraction. Following the above matching pattern, we decompose the
sentence-level similarity score into entity and context matching scores. Due to
the lack of explicit annotations of the redundant components, we design a
feature distillation module to adaptively identify the relation-irrelevant
features and reduce their negative impact on context matching. Experimental
results show that our method achieves higher matching $F_1$ score and has an
inference speed 10 times faster, when compared with the state-of-the-art
methods.

该研究主要针对零样本关系提取中的语义匹配问题，通过设计一种细粒度语义匹配方法，将句子级相似度分成实体和语境匹配分数，并通过特征蒸馏模块自适应识别与关系无关的特征，从而提高匹配率和推理速度。

RE-Matching：针对零样本关系提取的细粒度语义匹配方法

RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot  Relation Extraction

Image inpainting refers to the task of generating a complete, natural image
based on a partially revealed reference image. Recently, many research
interests have been focused on addressing this problem using fixed diffusion
models. These approaches typically directly replace the revealed region of the
intermediate or final generated images with that of the reference image or its
variants. However, since the unrevealed regions are not directly modified to
match the context, it results in incoherence between revealed and unrevealed
regions. To address the incoherence problem, a small number of methods
introduce a rigorous Bayesian framework, but they tend to introduce mismatches
between the generated and the reference images due to the approximation errors
in computing the posterior distributions. In this paper, we propose COPAINT,
which can coherently inpaint the whole image without introducing mismatches.
COPAINT also uses the Bayesian framework to jointly modify both revealed and
unrevealed regions, but approximates the posterior distribution in a way that
allows the errors to gradually drop to zero throughout the denoising steps,
thus strongly penalizing any mismatches with the reference image. Our
experiments verify that COPAINT can outperform the existing diffusion-based
methods under both objective and subjective metrics. The codes are available at
this https URL

本文介绍一种基于贝叶斯法的 COPAINT 算法，其使用扩散模型来修复图像并通过逐步减小误差的方式来减少与参考图像的偏差，从而优于现有的扩散模型方法。