Language Discrimination and Transfer Learning for Similar Languages: Experiments with Feature Combinations and Adaptation

Nianheng Wu, Eric DeMattos, Kwok Him So, Pin-zhen Chen, Çagri Çöltekin
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引用次数: 26

Abstract

This paper describes the work done by team tearsofjoy participating in the VarDial 2019 Evaluation Campaign. We developed two systems based on Support Vector Machines: SVM with a flat combination of features and SVM ensembles. We participated in all language/dialect identification tasks, as well as the Moldavian vs. Romanian cross-dialect topic identification (MRC) task. Our team achieved first place in German Dialect identification (GDI) and MRC subtasks 2 and 3, second place in the simplified variant of Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT) as well as Cuneiform Language Identification (CLI), and third and fifth place in DMT traditional and MRC subtask 1 respectively. In most cases, the SVM with a flat combination of features performed better than SVM ensembles. Besides describing the systems and the results obtained by them, we provide a tentative comparison between the feature combination methods, and present additional experiments with a method of adaptation to the test set, which may indicate potential pitfalls with some of the data sets.
相似语言的语言辨别与迁移学习:特征组合与适应实验
本文描述了tearsofjoy团队参加VarDial 2019评估活动所做的工作。我们开发了两个基于支持向量机的系统:具有平坦特征组合的支持向量机和支持向量机集合。我们参与了所有语言/方言识别任务,以及摩尔多瓦语和罗马尼亚语跨方言主题识别(MRC)任务。我们的团队在德语方言识别(GDI)和MRC子任务2和3中获得第一名,在普通话大陆和台湾变体的区分(DMT)简化变体和楔形文字识别(CLI)中分别获得第二名,在DMT传统和MRC子任务1中分别获得第三名和第五名。在大多数情况下,具有平坦特征组合的支持向量机比支持向量机集成的性能更好。除了描述系统及其获得的结果外,我们还提供了特征组合方法之间的初步比较,并提供了一种适应测试集的方法的附加实验,这可能表明某些数据集存在潜在的缺陷。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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