基于语境分隔符的网络语篇讽刺语检测
Sarcasm Detection using Context Separators in Online Discourse
摘要：
讽刺是一种复杂的言语形式，其含义是含蓄地传达的。作为一种复杂的表达形式，检测讽刺是一个艰巨的问题。识别讽刺的困难有很多陷阱，包括日常交流中的误解，这使得我们越来越关注自动讽刺检测。在第二版的比喻语言处理（FigLang 2020）研讨会中，讽刺检测的共享任务发布了两个数据集，包含来自Twitter和Reddit的响应及其上下文。在这项工作中，我们使用RoBERTa_large来检测两个数据集中的讽刺。我们进一步断言上下文的重要性，在提高性能的上下文词嵌入为基础的模型，通过使用三种不同类型的输入-响应，上下文响应，上下文响应（分离）。我们表明，我们提出的架构执行竞争力的两个数据集。我们还表明，在上下文和目标响应之间添加分离标记会导致Reddit数据集中F1分数提高5.13%。
英文摘要：
Sarcasm is an intricate form of speech, where meaning is conveyed implicitly. Being a convoluted form of expression, detecting sarcasm is an assiduous problem. The difficulty in recognition of sarcasm has many pitfalls, including misunderstandings in everyday communications, which leads us to an increasing focus on automated sarcasm detection. In the second edition of the Figurative Language Processing (FigLang 2020) workshop, the shared task of sarcasm detection released two datasets, containing responses along with their context sampled from Twitter and Reddit. In this work, we use RoBERTa_large to detect sarcasm in both the datasets. We further assert the importance of context in improving the performance of contextual word embedding based models by using three different types of inputs - Response-only, Context-Response, and Context-Response (Separated). We show that our proposed architecture performs competitively for both the datasets. We also show that the addition of a separation token between context and target response results in an improvement of 5.13% in the F1-score in the Reddit dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
