论文标题
使用多语言变压器模型的零射击跨语言目光跟踪数据预测
Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models
论文作者
论文摘要
阅读过程中的眼睛跟踪数据是了解语言理解过程中发生的认知过程的有用信息。不同的语言解释了不同的脑部触发因素，但是似乎有一些统一的指标。在本文中，我们描述了我们对CMCL 2022共享任务的提交，以预测多语言数据集的人类阅读模式。我们的模型使用来自变形金刚的文本表示形式和一些手工设计的功能，顶部具有回归层，以预测2个主要眼睛跟踪特征的均值和标准偏差的统计度量。我们训练一个端到端模型，从不同语言中提取有意义的信息，并在两个单独的数据集上测试我们的模型。我们比较了不同的变压器模型，并显示了影响模型性能的消融研究。我们的最终提交是子任务1和子任务的第四名，共享任务排名第四。
Eye tracking data during reading is a useful source of information to understand the cognitive processes that take place during language comprehension processes. Different languages account for different brain triggers , however there seems to be some uniform indicators. In this paper, we describe our submission to the CMCL 2022 shared task on predicting human reading patterns for multi-lingual dataset. Our model uses text representations from transformers and some hand engineered features with a regression layer on top to predict statistical measures of mean and standard deviation for 2 main eye-tracking features. We train an end to end model to extract meaningful information from different languages and test our model on two seperate datasets. We compare different transformer models and show ablation studies affecting model performance. Our final submission ranked 4th place for SubTask-1 and 1st place for SubTask-2 for the shared task.
