数据稀缺环境下的论点挖掘：跨语言迁移与小样本技术
Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques
AI总结：
本文实证表明，在数据稀缺的论点挖掘任务中，数据迁移优于模型迁移，微调优于小样本方法，且数据集领域、任务类型和采样方法是关键因素。
摘要：
最近关于序列标注的研究一直在探索不同的策略，以缓解世界上绝大多数语言缺乏人工标注数据的问题。其中，最成功的方法基于：多语言预训练语言模型的跨语言迁移能力（模型迁移，model-transfer）；数据翻译与标签投影（数据迁移，data-transfer）；以及通过复用掩码目标进行基于提示的学习，以利用预训练语言模型的小样本能力（few-shot）。先前的研究似乎得出结论：模型迁移优于数据迁移方法，且基于提示的小样本技术优于通过微调更新模型权重。在本文中，我们实证证明，对于论点挖掘这一需要检测长而复杂话语结构的序列标注任务，先前关于跨语言迁移或小样本学习的见解并不适用。与先前的工作相反，我们表明对于论点挖掘，数据迁移比模型迁移获得更好的结果，且微调优于小样本方法。关于前者，用于数据迁移的数据集领域似乎是一个决定性因素；而对于小样本，任务类型（序列跨度的长度和复杂性）和采样方法被证明是至关重要的。
英文摘要：
Recent research on sequence labelling has been exploring different strategies to mitigate the lack of manually annotated data for the large majority of the world languages. Among others, the most successful approaches have been based on (i) the cross-lingual transfer capabilities of multilingual pre-trained language models (model-transfer), (ii) data translation and label projection (data-transfer) and (iii), prompt-based learning by reusing the mask objective to exploit the few-shot capabilities of pre-trained language models (few-shot). Previous work seems to conclude that model-transfer outperforms data-transfer methods and that few-shot techniques based on prompting are superior to updating the model's weights via fine-tuning. In this paper, we empirically demonstrate that, for Argument Mining, a sequence labelling task which requires the detection of long and complex discourse structures, previous insights on cross-lingual transfer or few-shot learning do not apply. Contrary to previous work, we show that for Argument Mining data transfer obtains better results than model-transfer and that fine-tuning outperforms few-shot methods. Regarding the former, the domain of the dataset used for data-transfer seems to be a deciding factor, while, for few-shot, the type of task (length and complexity of the sequence spans) and sampling method prove to be crucial.
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。
