  Large-scale models for learning fixed-dimensional cross-lingual sentence
representations like LASER (Artetxe and Schwenk, 2019b) lead to significant
improvement in performance on downstream tasks. However, further increases and
modifications based on such large-scale models are usually impractical due to
memory limitations. In this work, we introduce a lightweight dual-transformer
architecture with just 2 layers for generating memory-efficient cross-lingual
sentence representations. We explore different training tasks and observe that
current cross-lingual training tasks leave a lot to be desired for this shallow
architecture. To ameliorate this, we propose a novel cross-lingual language
model, which combines the existing single-word masked language model with the
newly proposed cross-lingual token-level reconstruction task. We further
augment the training task by the introduction of two computationally-lite
sentence-level contrastive learning tasks to enhance the alignment of
cross-lingual sentence representation space, which compensates for the learning
bottleneck of the lightweight transformer for generative tasks. Our comparisons
with competing models on cross-lingual sentence retrieval and multilingual
document classification confirm the effectiveness of the newly proposed
training tasks for a shallow model.
