This paper proposes a novelty approach to mitigate the negative transfer problem. In the field of machine learning, the common strategy is to apply the Single-Task Learning approach in order to train a supervised model to solve a specific task. Training a robust model requires a lot of data and a significant amount of computational resources, making this solution unfeasible in cases where data are unavailable or expensive to gather. Therefore another solution, based on the sharing of information between tasks, has been developed: Multi-Task Learning (MTL). Despite the recent developments regarding MTL, the problem of negative transfer has still to be solved. Negative transfer is a phenomenon that occurs when noisy information is shared between tasks, resulting in a drop in performance. This paper proposes a new approach to mitigate the negative transfer problem based on the task awareness concept. The proposed approach results in diminishing the negative transfer together with an improvement of performance over classic MTL solution. Moreover, the proposed approach has been implemented in two unified architectures to detect Sexism, Hate Speech, and Toxic Language in text comments. The proposed architectures set a new state-of-the-art both in EXIST-2021 and HatEval-2019 benchmarks.

翻译：本文提出一种创新方法以缓解负迁移问题。在机器学习领域，常用策略是采用单任务学习方法来训练监督模型以解决特定任务。然而，训练鲁棒模型需要大量数据与显著计算资源，在数据不可获取或获取成本高昂的情况下，该方案往往难以实现。为此，研究者开发了基于任务间信息共享的多任务学习方案。尽管多任务学习近期取得进展，负迁移问题仍未得到有效解决。负迁移现象指任务间共享噪声信息导致性能下降的现象。本文基于任务感知概念，提出缓解负迁移问题的新方法。该方法在减少负迁移的同时，相较于经典多任务学习方案实现了性能提升。此外，本文在两种统一架构中实现所提方法，用于检测文本评论中的性别歧视、仇恨言论及恶意语言。所提架构在EXIST-2021与HatEval-2019基准测试中均刷新了当前最优性能记录。