We explore the problem of predicting the publication period of text document,
such as a news article, using the text from that document. In order to do so,
we created our own extensive labeled dataset of over 350,000 news articles
published by The New York Times over six decades. We then provide an
implementation of a simple Naive Bayes baseline model, which surprisingly
achieves decent performance in terms of accuracy.Finally, for our approach, we
use a pretrained BERT model fine-tuned for the task of text classification.
This model exceeds our expectations and provides some very impressive results
in terms of accurately classifying news articles into their respective
publication decades. The results beat the performance of the few previously
tried models for this relatively unexplored task of time prediction from text.

本文旨在通过使用文本文档预测其发布期，使用了自行创建的全面标记的数据集，并实现了一个简单的朴素贝叶斯基线模型和一个经过微调的预训练 BERT 模型，而结果则超出了我们的预期，在准确预测新闻文章发布年代方面表现出色，击败了此前在此相对未开发任务中尝试过的少数模型。

基于 Transformer 的文章时段预测模型

Text2Time: Transformer-based article time period predictor

The quality of training data is one of the crucial problems when a
learning-centered approach is employed. This paper proposes a new method to
investigate the quality of a large corpus designed for the recognizing textual
entailment (RTE) task. The proposed method, which is inspired by a statistical
hypothesis test, consists of two phases: the first phase is to introduce the
predictability of textual entailment labels as a null hypothesis which is
extremely unacceptable if a target corpus has no hidden bias, and the second
phase is to test the null hypothesis using a Naive Bayes model. The
experimental result of the Stanford Natural Language Inference (SNLI) corpus
does not reject the null hypothesis. Therefore, it indicates that the SNLI
corpus has a hidden bias which allows prediction of textual entailment labels
from hypothesis sentences even if no context information is given by a premise
sentence. This paper also presents the performance impact of NN models for RTE
caused by this hidden bias.

本文提出了一种新的方法来研究用于识别文本蕴含任务的大型语料库的质量，通过基于统计假设检验的方法，测试了斯坦福自然语言推理（SNLI）语料库的可预测性，结果表明该库存在隐藏的偏差，并呈现了此偏差对于 RTE 的 NN 模型性能的影响。

训练数据中的隐藏偏差对文本蕴涵识别性能的影响

Performance Impact Caused by Hidden Bias of Training Data for  Recognizing Textual Entailment

We study the decomposition of a nonnegative tensor into a minimal sum of
outer product of nonnegative vectors and the associated parsimonious naive
Bayes probabilistic model. We show that the corresponding approximation
problem, which is central to nonnegative PARAFAC, will always have optimal
solutions. The result holds for any choice of norms and, under a mild
assumption, even Bregman divergences.

研究对一个非负张量进行分解为非负向量的最小外积和对应的简化的朴素贝叶斯概率模型，并证明该问题的逼近问题，无论采用哪种规范，甚至是布雷格曼分歧，都将始终具有最优解。