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: this https URL.

本文介绍了 BLCU-ICALL 系统在 SemEval-2022 任务 1 中使用的 transformer-based multitasking framework，该框架通过交叉注意机制集成多种嵌入模型并通过掩码语言模型目标捕捉词条结构，同时还研究了一种简单有效的模型组合策略以提高鲁棒性，评价结果表明我们的方案非常有效。