Text generation tasks have gotten the attention of researchers in the last
few years because of their applications on a large this http URL the past, many
researchers focused on task-based text generations.Our research focuses on text
generation based on the ideology and style of a specific author, and text
generation on a topic that was not written by the same author in the past.Our
trained model requires an input prompt containing initial few words of text to
produce a few paragraphs of text based on the ideology and style of the author
on which the model is trained.Our methodology to accomplish this task is based
on Bi-LSTM.The Bi-LSTM model is used to make predictions at the character
level, during the training corpus of a specific author is used along with the
ground truth corpus.A pre-trained model is used to identify the sentences of
ground truth having contradiction with the author's corpus to make our language
model inclined.During training, we have achieved a perplexity score of 2.23 at
the character level. The experiments show a perplexity score of around 3 over
the test dataset.

该研究使用 Bi-LSTM 模型基于特定作者的思想和风格生成主题非该作者编写的一篇文章。在训练语料库中包含特定作者的作品和现成的真实语料库以及使用预训练模型识别矛盾的语句，实现了字符级别的 perplexity 得分。

使用深度学习克隆意识形态和风格

Cloning Ideology and Style using Deep Learning

Few-shot learning has drawn researchers' attention to overcome the problem of
data scarcity. Recently, large pre-trained language models have shown great
performance in few-shot learning for various downstream tasks, such as question
answering and machine translation. Nevertheless, little exploration has been
made to achieve few-shot learning for the fact-checking task. However,
fact-checking is an important problem, especially when the amount of
information online is growing exponentially every day. In this paper, we
propose a new way of utilizing the powerful transfer learning ability of a
language model via a perplexity score. The most notable strength of our
methodology lies in its capability in few-shot learning. With only two training
samples, our methodology can already outperform the Major Class baseline by
more than absolute 10% on the F1-Macro metric across multiple datasets. Through
experiments, we empirically verify the plausibility of the rather surprising
usage of the perplexity score in the context of fact-checking and highlight the
strength of our few-shot methodology by comparing it to strong
fine-tuning-based baseline models. Moreover, we construct and publicly release
two new fact-checking datasets related to COVID-19.

本文提出了一种利用语言模型的强大迁移学习能力来实现 fact-checking 任务的 few-shot learning 新方法，并通过实验证明了该方法在多个数据集上取得了非常不错的效果，并公开了两个 COVID-19 相关的事实检查数据集。