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 objective of this paper is a temporal alignment network that ingests long
term video sequences, and associated text sentences, in order to: (1) determine
if a sentence is alignable with the video; and (2) if it is alignable, then
determine its alignment. The challenge is to train such networks from
large-scale datasets, such as HowTo100M, where the associated text sentences
have significant noise, and are only weakly aligned when relevant. Apart from
proposing the alignment network, we also make four contributions: (i) we
describe a novel co-training method that enables to denoise and train on raw
instructional videos without using manual annotation, despite the considerable
noise; (ii) to benchmark the alignment performance, we manually curate a
10-hour subset of HowTo100M, totalling 80 videos, with sparse temporal
descriptions. Our proposed model, trained on HowTo100M, outperforms strong
baselines (CLIP, MIL-NCE) on this alignment dataset by a significant margin;
(iii) we apply the trained model in the zero-shot settings to multiple
downstream video understanding tasks and achieve state-of-the-art results,
including text-video retrieval on YouCook2, and weakly supervised video action
segmentation on Breakfast-Action; (iv) we use the automatically aligned
HowTo100M annotations for end-to-end finetuning of the backbone model, and
obtain improved performance on downstream action recognition tasks.

本文提出了一种时间对齐网络，能够对长期视频序列和关联文本句子进行处理，使用了一种新颖的共同训练方法来减少噪音并训练原始指导视频，应用于多个视频理解任务，包括文本 - 视频检索和弱监督视频动作分割等，获得了更好的性能。

长时间视频的时间对齐网络

Temporal Alignment Networks for Long-term Video

Accuracy is one of the basic principles of journalism. However, it is
increasingly hard to manage due to the diversity of news media. Some editors of
online news tend to use catchy headlines which trick readers into clicking.
These headlines are either ambiguous or misleading, degrading the reading
experience of the audience. Thus, identifying inaccurate news headlines is a
task worth studying. Previous work names these headlines "clickbaits" and
mainly focus on the features extracted from the headlines, which limits the
performance since the consistency between headlines and news bodies is
underappreciated. In this paper, we clearly redefine the problem and identify
ambiguous and misleading headlines separately. We utilize class sequential
rules to exploit structure information when detecting ambiguous headlines. For
the identification of misleading headlines, we extract features based on the
congruence between headlines and bodies. To make use of the large unlabeled
data set, we apply a co-training method and gain an increase in performance.
The experiment results show the effectiveness of our methods. Then we use our
classifiers to detect inaccurate headlines crawled from different sources and
conduct a data analysis.

本研究通过重新定义问题、识别模糊和具有误导性的标题，并利用类序贯规则和共同训练的方法，成功筛选出来自不同来源的不准确标题，并进行数据分析。