EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning
This addresses the problem of cross-lingual understanding for NLP applications, offering a novel method that is incremental in improving representation learning.
The paper tackles the challenge of learning universal cross-lingual representations despite sparse parallel data by proposing EMMA-X, an EM-like multilingual pre-training algorithm that uses non-parallel data and achieves state-of-the-art performance on the XRETE benchmark with 12 cross-lingual tasks.
Expressing universal semantics common to all languages is helpful in understanding the meanings of complex and culture-specific sentences. The research theme underlying this scenario focuses on learning universal representations across languages with the usage of massive parallel corpora. However, due to the sparsity and scarcity of parallel data, there is still a big challenge in learning authentic ``universals'' for any two languages. In this paper, we propose EMMA-X: an EM-like Multilingual pre-training Algorithm, to learn (X)Cross-lingual universals with the aid of excessive multilingual non-parallel data. EMMA-X unifies the cross-lingual representation learning task and an extra semantic relation prediction task within an EM framework. Both the extra semantic classifier and the cross-lingual sentence encoder approximate the semantic relation of two sentences, and supervise each other until convergence. To evaluate EMMA-X, we conduct experiments on XRETE, a newly introduced benchmark containing 12 widely studied cross-lingual tasks that fully depend on sentence-level representations. Results reveal that EMMA-X achieves state-of-the-art performance. Further geometric analysis of the built representation space with three requirements demonstrates the superiority of EMMA-X over advanced models.