Sarcasm is a form of irony that requires readers or listeners to interpret
its intended meaning by considering context and social cues. Machine learning
classification models have long had difficulty detecting sarcasm due to its
social complexity and contradictory nature.
This paper explores the applications of the Generative Pretrained Transformer
(GPT) models, including GPT-3, InstructGPT, GPT-3.5, and GPT-4, in detecting
sarcasm in natural language. It tests fine-tuned and zero-shot models of
different sizes and releases.
The GPT models were tested on the political and balanced (pol-bal) portion of
the popular Self-Annotated Reddit Corpus (SARC 2.0) sarcasm dataset. In the
fine-tuning case, the largest fine-tuned GPT-3 model achieves accuracy and
$F_1$-score of 0.81, outperforming prior models. In the zero-shot case, one of
GPT-4 models yields an accuracy of 0.70 and $F_1$-score of 0.75. Other models
score lower. Additionally, a model's performance may improve or deteriorate
with each release, highlighting the need to reassess performance after each
release.

本研究探讨了生成预训练转换器（GPT）模型（包括 GPT-3、InstructGPT、GPT-3.5 和 GPT-4）在检测自然语言中的讽刺的应用。该研究测试了不同大小和版本的精调和零 - shot 模型，并在 Self-Annotated Reddit Corpus（SARC 2.0）的政治和均衡（pol-bal）部分的讽刺数据集上进行了评估。结果显示，在精调的情况下，最大的精调 GPT-3 模型在准确性和 F1-score 方面达到了 0.81，超过了之前的模型。在零 - shot 的情况下，其中一个 GPT-4 模型的准确性为 0.70，F1-score 为 0.75，其他模型得分较低。此外，每个版本的模型性能可能会有所改善或恶化，强调了需要在每个版本发布后重新评估性能的必要性。

基于 OpenAI GPT 模型的讽刺检测研究

On Sarcasm Detection with OpenAI GPT-based Models

Irony is a ubiquitous figurative language in daily communication. Previously,
many researchers have approached irony from linguistic, cognitive science, and
computational aspects. Recently, some progress have been witnessed in automatic
irony processing due to the rapid development in deep neural models in natural
language processing (NLP). In this paper, we will provide a comprehensive
overview of computational irony, insights from linguistic theory and cognitive
science, as well as its interactions with downstream NLP tasks and newly
proposed multi-X irony processing perspectives.

本文综述了计算机语言学中讽刺的研究进展，以及从语言学和认知科学角度的深入解读，同时还提出了讽刺与下游 NLP 任务和多重 X 讽刺处理视角的交互作用。

自动讽刺处理的调研：语言，认知和多元视角

A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives

We introduce a collection of recognizing textual entailment (RTE) datasets
focused on figurative language. We leverage five existing datasets annotated
for a variety of figurative language -- simile, metaphor, and irony -- and
frame them into over 12,500 RTE examples.We evaluate how well state-of-the-art
models trained on popular RTE datasets capture different aspects of figurative
language. Our results and analyses indicate that these models might not
sufficiently capture figurative language, struggling to perform pragmatic
inference and reasoning about world knowledge. Ultimately, our datasets provide
a challenging testbed for evaluating RTE models.

集合了五个 RTE 数据集的文章，聚焦于比喻语言，示例超过 12500 个，研究发现现有的 RTE 模型无法很好地捕捉比喻语言的不同方面，无法进行语用推断和世界知识推理。该研究提供了一个挑战性的测试平台来评估 RTE 模型。

文本蕴涵中的比喻语言

Figurative Language in Recognizing Textual Entailment

The use of irony and sarcasm in social media allows us to study them at scale
for the first time. However, their diversity has made it difficult to construct
a high-quality corpus of sarcasm in dialogue. Here, we describe the process of
creating a large- scale, highly-diverse corpus of online debate forums
dialogue, and our novel methods for operationalizing classes of sarcasm in the
form of rhetorical questions and hyperbole. We show that we can use
lexico-syntactic cues to reliably retrieve sarcastic utterances with high
accuracy. To demonstrate the properties and quality of our corpus, we conduct
supervised learning experiments with simple features, and show that we achieve
both higher precision and F than previous work on sarcasm in debate forums
dialogue. We apply a weakly-supervised linguistic pattern learner and
qualitatively analyze the linguistic differences in each class.

本文描述了如何通过构建一个大规模、高度多样化的线上辩论论坛对话语料库及其对讽刺言语两种分类构建创新方法，使用词汇 - 语法线索可准确检索出讽刺言语，并在简单特征和弱监督自然语言模式学习的实验中得到了较高的表现。