Struct-XLM: A Structure Discovery Multilingual Language Model for Enhancing Cross-lingual Transfer through Reinforcement Learning

Abstract

Cross-lingual transfer learning heavily relies on well-aligned cross-lingual representations. The syntactic structure is recognized as beneficial for cross-lingual transfer, but limited researches utilize it for aligning representation in multilingual pre-trained language models (PLMs). Additionally, existing methods require syntactic labels that are difficult to obtain and of poor quality for low-resource languages. To address this gap, we propose Struct-XLM, a novel multilingual language model that leverages reinforcement learning (RL) to autonomously discover universal syntactic structures for improving the cross-lingual representation alignment of PLM. Struct-XLM integrates a policy network (PNet) and a translation ranking task. The PNet is designed to discover structural information and integrate it into the last layer of the PLM through the structural multi-head attention module to obtain structural representation. The translation ranking task obtains a delayed reward based on the structural representation to optimize the PNet while improving the alignment of cross-lingual representation. Experiments show the effectiveness of the proposed approach for enhancing cross-lingual transfer of multilingual PLM on the XTREME benchmark.