Recent relation extraction (RE) works have shown encouraging improvements by
conducting contrastive learning on silver labels generated by distant
supervision before fine-tuning on gold labels. Existing methods typically
assume all these silver labels are accurate and treat them equally; however,
distant supervision is inevitably noisy -- some silver labels are more reliable
than others. In this paper, we propose fine-grained contrastive learning
(FineCL) for RE, which leverages fine-grained information about which silver
labels are and are not noisy to improve the quality of learned relationship
representations for RE. We first assess the quality of silver labels via a
simple and automatic approach we call "learning order denoising," where we
train a language model to learn these relations and record the order of learned
training instances. We show that learning order largely corresponds to label
accuracy -- early-learned silver labels have, on average, more accurate labels
than later-learned silver labels. Then, during pre-training, we increase the
weights of accurate labels within a novel contrastive learning objective.
Experiments on several RE benchmarks show that FineCL makes consistent and
significant performance gains over state-of-the-art methods.

本文提出了 FineCL 方法，即利用细粒度信息来提高关系提取中学习关系表示的质量，该方法通过训练语言模型以获得标签顺序并判断标签准确性，在预训练中增加准确标签的权重，实验结果表明 FineCL 方法在多个关系提取基准测试任务中都取得了显著的性能提升。

关系抽取的细粒度对比学习

Fine-grained Contrastive Learning for Relation Extraction

Approaches for the stance classification task, an important task for
understanding argumentation in debates and detecting fake news, have been
relying on models which deal with individual debate topics. In this paper, in
order to train a system independent from topics, we propose a new method to
extract data with silver labels from raw text to finetune a model for stance
classification. The extraction relies on specific discourse relation
information, which is shown as a reliable and accurate source for providing
stance information. We also propose a 3-stage training framework where the
noisy level in the data used for finetuning decreases over different stages
going from the most noisy to the least noisy. Detailed experiments show that
the automatically annotated dataset as well as the 3-stage training help
improve model performance in stance classification. Our approach ranks 1st
among 26 competing teams in the stance classification track of the NLPCC 2021
shared task Argumentative Text Understanding for AI Debater, which confirms the
effectiveness of our approach.

本论文提出了一种新的方法，通过提取特定的语篇关系信息从原始文本中提取带有银标签的数据，以微调用于态度分类的模型并提出了一个 3 阶段的训练框架，实现了从最嘈杂的数据到最少噪音的数据不断降噪。实验结果表明，自动注释的数据集以及 3 阶段训练有助于改善模型在态度分类中的性能，该方法在 NLPCC 2021 共 26 支队伍竞争的观点分类赛道中排名第一，验证了我们方法的有效性。

运用语篇关系进行距离微调以进行立场分类

Distant finetuning with discourse relations for stance classification

Most rumour detection models for social media are designed for one specific
language (mostly English). There are over 40 languages on Twitter and most
languages lack annotated resources to build rumour detection models. In this
paper we propose a zero-shot cross-lingual transfer learning framework that can
adapt a rumour detection model trained for a source language to another target
language. Our framework utilises pretrained multilingual language models (e.g.\
multilingual BERT) and a self-training loop to iteratively bootstrap the
creation of ''silver labels'' in the target language to adapt the model from
the source language to the target language. We evaluate our methodology on
English and Chinese rumour datasets and demonstrate that our model
substantially outperforms competitive benchmarks in both source and target
language rumour detection.

本研究提出了一种零样本跨语言迁移学习框架，利用预训练的多语言语言模型和自我训练循环来逐步引导目标语言中 ' 银标签 ' 的创建，从而将已训练好的谣言检测模型从源语言适应到另一种目标语言。在英语和汉语谣言数据集上进行评估，证明了该模型在源语言和目标语言的谣言检测方面均明显优于竞争基准。