MTLAN: Multi-Task Learning and Auxiliary Network for Enhanced Sentence Embedding

The objective of cross-lingual sentence embedding learning is to map sentences into a shared representation space, where semantically similar sentence representations are closer together, while distinct sentence representations exhibit clear differentiation. This paper proposes a novel sentence embedding model called MTLAN, which incorporates multi-task learning and auxiliary networks. The model utilizes the LaBSE model for extracting sentence features and undergoes joint training on tasks related to sentence semantic representation and distance measurement. Furthermore, an auxiliary network is employed to enhance the contextual expression of words within sentences. To address the issue of limited resources for low-resource languages, we construct a pseudocorpus dataset using a multilingual dictionary for unsupervised learning. We conduct experiments on multiple publicly available datasets, including STS and SICK, to evaluate both monolingual sentence similarity and cross-lingual semantic similarity. The empirical results demonstrate the significant superiority of our proposed model over state-of-the-art methods.