  This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1
Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack,
achieving 1st on Italian, 2nd on Spanish and Russian, and 3rd on English and
French. We propose a transformer-based multitasking framework to explore the
task. The framework integrates multiple embedding architectures through the
cross-attention mechanism, and captures the structure of glosses through a
masking language model objective. Additionally, we also investigate a simple
but effective model ensembling strategy to further improve the robustness. The
evaluation results show the effectiveness of our solution. We release our code
at: https://github.com/blcuicall/SemEval2022-Task1-DM.

Computation and Language