Multilingual Table Question Answering for Low-Resource Languages via Selective Question Translation and Cross-Lingual Adaptation

Table question answering datasets have been released in various table sources and diverse tasks. However, most of these table question answering datasets are constructed in resource-rich languages like English. For low-resource languages, it is necessary to create new datasets or translate existing ones to train models on table question answering tasks. This process requires substantial costs and manpower. Table data, unlike natural text, is structured in a way that is less affected by word order and language-specific characteristics. In this paper, we propose a method to minimize the costs associated with translating and applying new table question answering datasets. By leveraging the characteristics of table data, we suggest translating only the question data of the question answering table datasets into multiple languages. To minimize the performance degradation caused by using the native table data of the original dataset and not directly utilizing the table data in the target language, we applied adaptation methods using contrastive learning and adversarial training. To enhance the reasoning capabilities required for table question answering without diminishing performance in languages other than the one used for pre-training, we further pre-trained a RoBERTa-based multilingual encoder on table data using SQL data. The proposed cross-lingual adaptation demonstrated performance improvements in most of the languages evaluated. Additionally, the pre-training using SQL data significantly improved performance across all languages, even though the pre-training table data consisted of English data.