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# Generate a title for a paper to be published in a top journal | [Start Chat](https://gptcall.net/chat.html?data=%7B%22contact%22%3A%7B%22id%22%3A%22vhQzDdsROClOHUOI1VPFn%22%2C%22flow%22%3Atrue%7D%7D)
All you have to do is fill in the abstract and keywords of your article in the right place in my template, and you can let chatgpt give you a title for your paper that can be sent to important journals。

# Prompt

```
I will provide you with the abstract and key words of a scientific paper in any language and you will detect the language and reply in the same language. Your task is to provide me with the title of the scientific paper based on the abstract and key words in thevsame language. The title of the scientific paper should be concise, clear and informative. You should avoid using wasted words such as "a study of," "investigation of," "development of," or"observations on." Make sure the title can grip the audience immediately. My abstract is"事件抽取旨在将非结构化自然语言文本中的事件信息以结构化形式进行识别提取。传统事件抽取方法抽取范围局限
于单个句子，且依赖较大规模的标注数据，在篇章级抽取任务与低资源目标领域上性能不足。现有研究利用提示学习方法，以模板槽位填空方式实现篇章级事件抽取，其缺点在于传统提示模板槽位对论元角色分类准确度不足，容易造成论元角色抽取错误。针对上述问题，提出一种基于槽位语义增强提示学习的篇章级事件抽取方法，在提示学习方法的基础上，将传统事件抽取范式中的论元角色语义信息融入提示模板槽位中，为模型的槽位预测生成环节提供论元类型约束，提高篇章级事件抽取准确率。通过使预训练语言模型上下游任务保持一致，模型具有更好的泛化能力，并能够以较低成本实现知识迁移，在低资源事件抽取场景下取得了更好的性能。实验结果表明，相较于传统基线方法，该方法在包含 59 种论元类型的英文事件抽取数据集上获得了 2.6 的 F1 提升，在包含 92 类论元类型的中文数据集上获得了 2.9 的 F1 提升，并在低资源数据规模下获得了4.0 的 F1 提升。" My key words are "事件抽取；提示学习；信息抽取；自然语言处理；预训练语言模型"
```

## Conversation

**ASSISTANT**: 标题：基于槽位语义增强提示学习的篇章级事件抽取方法



该研究通过利用预训练语言模型和提示学习方法，提出了一种基于槽位语义增强的篇章级事件抽取方法，旨在解决传统事件抽取方法范围局限、依赖大规模标注数据的问题，在低资源领域下表现更优。实验结果表明，该方法相较于传统基线方法，在英文事件抽取数据集上获得 2.6 的 F1 提升，在中文数据集上获得 2.9 的 F1 提升，并在低资源数据规模下获得了 4.0 的 F1 提升。此方法具有实际应用价值，可为自然语言处理领域相关研究提供借鉴和参考。关键词包括“事件抽取”、“提示学习”、“信息抽取”、“自然语言处理”和“预训练语言模型”。
**USER**: I will provide you with the abstract and key words of a scientific paper in any language and you will detect the language and reply in the same language. Your task is to provide me with the title of the scientific paper based on the abstract and key words in thevsame language. The title of the scientific paper should be concise, clear and informative. You should avoid using wasted words such as "a study of," "investigation of," "development of," or"observations on." Make sure the title can grip the audience immediately. My abstract is"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data." My key words are ""
**ASSISTANT**: 这篇论文的标题是：Transformer模型：基于注意力机制的神经网络序列转换模型



该研究提出了一种新的简单的网络结构——Transformer模型，它仅基于注意力机制而不使用循环和卷积，解决了传统序列转换模型在编码器-解码器架构中过于复杂的问题。实验结果表明，这种模型比现有最佳模型效果更好，在训练时间、可并行性等方面也更具优势。在WMT2014英德翻译任务上，本模型的BLEU得分达到28.4，优于现有最佳结果，超过2分，证明其性能优异。此外，在WMT2014英法翻译任务上，本模型在只用8个GPU进行3.5天训练后，取得了41.8的BLEU分数，成为同类模型领域的最新标杆。研究还验证了Transformer模型在其他任务上的泛化能力，成功将其应用于英语句法分析中。关键词未提供。


