Macular: A Multi-Task Adversarial Framework for Cross-Lingual Natural Language Understanding

Cross-lingual natural language understanding (NLU) aims to train NLU models on a source language and apply the models to NLU tasks in target languages, and is a fundamental task for many cross-language applications. Most of the existing cross-lingual NLU models assume the existence of parallel corpora so that words and sentences in source and target languages could be aligned. However, the construction of such parallel corpora is expensive and sometimes infeasible. Motivated by this challenge, recent works propose data augmentation or adversarial training methods to reduce the reliance on external parallel corpora. In this paper, we propose an orthogonal and novel perspective to tackle this challenging cross-lingual NLU task (i.e., when parallel corpora are unavailable). We propose to conduct multi-task learning across different tasks for mutual performance improvement on both source and target languages. The proposed multi-task learning framework is complementary to existing studies and could be integrated with existing methods to further improve their performance on challenging cross-lingual NLU tasks.