Simile detection is a valuable task for many natural language processing
(NLP)-based applications, particularly in the field of literature. However,
existing research on simile detection often relies on corpora that are limited
in size and do not adequately represent the full range of simile forms. To
address this issue, we propose a simile data augmentation method based on
\textbf{W}ord replacement And Sentence completion using the GPT-2 language
model. Our iterative process called I-WAS, is designed to improve the quality
of the augmented sentences. To better evaluate the performance of our method in
real-world applications, we have compiled a corpus containing a more diverse
set of simile forms for experimentation. Our experimental results demonstrate
the effectiveness of our proposed data augmentation method for simile
detection.

通过基于 GPT-2 语言模型的单词替换和句子完成的拟态数据增强方法，本研究提出了一种改进的方法 I-WAS，旨在改善拟态句子的质量，为真实世界的应用提供更多样化的拟态形式的语料库，并通过实验结果证明了该数据增强方法在拟态检测中的有效性。

I-WAS: GPT-2 进行明喻检测的数据增强方法

I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection

We introduce a zero-shot video captioning method that employs two frozen
networks: the GPT-2 language model and the CLIP image-text matching model. The
matching score is used to steer the language model toward generating a sentence
that has a high average matching score to a subset of the video frames. Unlike
zero-shot image captioning methods, our work considers the entire sentence at
once. This is achieved by optimizing, during the generation process, part of
the prompt from scratch, by modifying the representation of all other tokens in
the prompt, and by repeating the process iteratively, gradually improving the
specificity and comprehensiveness of the generated sentence. Our experiments
show that the generated captions are coherent and display a broad range of
real-world knowledge. Our code is available at:
this https URL

本文提出了一种零样本视频字幕生成方法，采用了冻结的 GPT-2 语言模型和 CLIP 图像 - 文本匹配模型，并通过生成过程中的优化方法生成了连贯、具有广泛实际知识的视频字幕。