数据高效的仇恨言论检测：基于跨语言最近邻检索与有限标注数据
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data
AI总结：
本研究提出一种基于最近邻检索的跨语言迁移学习方法，利用目标语言少量标注数据从多语言池中检索相关样本，以数据高效且可扩展的方式提升仇恨言论检测性能，并在八种语言上超越现有方法。
摘要：
鉴于检测仇恨言论的重要性，标注仇恨言论数据的收集成本高昂且耗时，尤其是对于低资源语言。先前的研究已证明跨语言迁移学习和数据增强在改善有限标注数据任务性能方面的有效性。为了开发一种高效且可扩展的跨语言迁移学习方法，我们利用最近邻检索来增强目标语言中的最小标注数据，从而提升检测性能。具体而言，我们假设可以访问目标语言中的一小部分标注训练实例，并利用这些实例从大型多语言仇恨言论检测池中检索最相关的标注示例。我们在八种语言上评估了我们的方法，并证明其始终优于仅使用目标语言数据训练的模型。此外，在大多数情况下，我们的方法超越了当前最先进的技术水平。值得注意的是，我们的方法高度数据高效，在某些情况下仅检索200个实例即可保持优越性能。而且，该方法具有可扩展性，因为检索池可以轻松扩展，且该方法可以轻松适应新语言和新任务。我们还应用最大边际相关性来减少冗余并过滤掉高度相似的检索实例，这在某些语言上带来了改进。
英文摘要：
Considering the importance of detecting hateful language, labeled hate speech data is expensive and time-consuming to collect, particularly for low-resource languages. Prior work has demonstrated the effectiveness of cross-lingual transfer learning and data augmentation in improving performance on tasks with limited labeled data. To develop an efficient and scalable cross-lingual transfer learning approach, we leverage nearest-neighbor retrieval to augment minimal labeled data in the target language, thereby enhancing detection performance. Specifically, we assume access to a small set of labeled training instances in the target language and use these to retrieve the most relevant labeled examples from a large multilingual hate speech detection pool. We evaluate our approach on eight languages and demonstrate that it consistently outperforms models trained solely on the target language data. Furthermore, in most cases, our method surpasses the current state-of-the-art. Notably, our approach is highly data-efficient, retrieving as small as 200 instances in some cases while maintaining superior performance. Moreover, it is scalable, as the retrieval pool can be easily expanded, and the method can be readily adapted to new languages and tasks. We also apply maximum marginal relevance to mitigate redundancy and filter out highly similar retrieved instances, resulting in improvements in some languages.
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