论文标题
用变压器适配器缩放母语标识
Scaling Native Language Identification with Transformer Adapters
论文作者
论文摘要
本地语言识别（NLI）是根据学习语言自动识别个人的语言生产的母语（L1）的任务。它对包括营销，安全和教育应用在内的各种目的都有用。 NLI通常被构成多标签分类任务，在其中组合了许多设计的功能以实现最新的结果。最近，基于变压器解码器（GPT-2）的深层生成方法优于其对应物，并在NLI基准数据集上取得了最佳结果。我们研究了这种方法，以确定与传统最先进的NLI系统相比的实际含义。我们介绍了变压器适配器，以解决记忆限制并提高训练/推理速度以扩展NLI的生产应用程序。
Native language identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is useful for a variety of purposes including marketing, security and educational applications. NLI is usually framed as a multi-label classification task, where numerous designed features are combined to achieve state-of-the-art results. Recently deep generative approach based on transformer decoders (GPT-2) outperformed its counterparts and achieved the best results on the NLI benchmark datasets. We investigate this approach to determine the practical implications compared to traditional state-of-the-art NLI systems. We introduce transformer adapters to address memory limitations and improve training/inference speed to scale NLI applications for production.
