Multilingual Argument Mining: Datasets and Analysis
This addresses the language gap in argument mining for researchers and practitioners, but it is incremental as it builds on existing transfer learning methods.
The authors tackled the lack of non-English resources in argument mining by exploring transfer learning with multilingual BERT and machine translation, showing it works well for stance classification and evidence detection but poorly for quality assessment, and they released a multilingual dataset with over 10k arguments.
The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However, as with many other NLU tasks, the dominant language is English, with resources in other languages being few and far between. In this work, we explore the potential of transfer learning using the multilingual BERT model to address argument mining tasks in non-English languages, based on English datasets and the use of machine translation. We show that such methods are well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments, presumably because quality is harder to preserve under translation. In addition, focusing on the translate-train approach, we show how the choice of languages for translation, and the relations among them, affect the accuracy of the resultant model. Finally, to facilitate evaluation of transfer learning on argument mining tasks, we provide a human-generated dataset with more than 10k arguments in multiple languages, as well as machine translation of the English datasets.