Scientific Natural Language Inference (NLI) is the task of predicting the
semantic relation between a pair of sentences extracted from research articles.
The automatic annotation method based on distant supervision for the training
set of SciNLI (Sadat and Caragea, 2022b), the first and most popular dataset
for this task, results in label noise which inevitably degenerates the
performance of classifiers. In this paper, we propose a novel co-training
method that assigns weights based on the training dynamics of the classifiers
to the distantly supervised labels, reflective of the manner they are used in
the subsequent training epochs. That is, unlike the existing semi-supervised
learning (SSL) approaches, we consider the historical behavior of the
classifiers to evaluate the quality of the automatically annotated labels.
Furthermore, by assigning importance weights instead of filtering out examples
based on an arbitrary threshold on the predicted confidence, we maximize the
usage of automatically labeled data, while ensuring that the noisy labels have
a minimal impact on model training. The proposed method obtains an improvement
of 1.5% in Macro F1 over the distant supervision baseline, and substantial
improvements over several other strong SSL baselines. We make our code and data
available on Github.

我们提出了一种新的协同训练方法，通过根据分类器的训练动态为远程监督标签分配权重，反映了它们在后续训练轮次中的使用方式。我们通过分配重要权重而不是基于预测置信度的任意阈值过滤示例，使得自动标记的数据得以最大化利用，同时确保噪声标签对模型训练的影响最小化。该方法相对于远程监督基准方法在 Macro F1 上实现了 1.5％的改进，并在几个其他强 SSL 基准方法上取得了显著的改进。我们在 Github 上提供了我们的代码和数据。

低资源科学自然语言推理的共同训练

Co-training for Low Resource Scientific Natural Language Inference

The task of scientific Natural Language Inference (NLI) involves predicting
the semantic relation between two sentences extracted from research articles.
This task was recently proposed along with a new dataset called SciNLI derived
from papers published in the computational linguistics domain. In this paper,
we aim to introduce diversity in the scientific NLI task and present MSciNLI, a
dataset containing 132,320 sentence pairs extracted from five new scientific
domains. The availability of multiple domains makes it possible to study domain
shift for scientific NLI. We establish strong baselines on MSciNLI by
fine-tuning Pre-trained Language Models (PLMs) and prompting Large Language
Models (LLMs). The highest Macro F1 scores of PLM and LLM baselines are 77.21%
and 51.77%, respectively, illustrating that MSciNLI is challenging for both
types of models. Furthermore, we show that domain shift degrades the
performance of scientific NLI models which demonstrates the diverse
characteristics of different domains in our dataset. Finally, we use both
scientific NLI datasets in an intermediate task transfer learning setting and
show that they can improve the performance of downstream tasks in the
scientific domain. We make our dataset and code available on Github.

科学自然语言推理（NLI）的任务涉及预测从研究文章中提取的两个句子之间的语义关系。本文介绍了多样性科学 NLI 任务，并提出了 MSciNLI，这是一个包含 132,320 个从五个新科学领域中提取的句子配对的数据集。通过精调预训练语言模型和促使大型语言模型建立了 MSciNLI 的强基准。最高的 PLM 和 LLM 基准的宏 F1 分数分别为 77.21% 和 51.77%，说明 MSciNLI 对这两种模型来说都具有挑战性。此外，我们证明了领域转移降低了科学 NLI 模型的性能，从而证明了我们数据集中不同领域的多样性特征。最后，我们在中间任务迁移学习设置中使用了两个科学 NLI 数据集，并展示了它们可以提高科学领域下游任务的性能。我们在 Github 上提供了数据集和代码。