Multi-task learning (MTL) has succeeded in various industrial applications by utilizing common knowledge among joint training tasks to enhance the generalization of MTL models, resulting in improved performance across all training tasks simultaneously. Unfortunately, training all tasks simultaneously often causes performance degradation compared to single-task models since different tasks might conflict with each other. Despite existing MTL methods that aim to mitigate task conflicts by manipulating task gradients at each iteration, they ignore the potential influence of noisy data from different batches on task gradients. Consequently, the current iteration's task gradient may not accurately reflect the task itself, leading to inadequate alleviation of the dilemma of task conflicts. Moreover, existing works seldom explore the potential source of task conflicts and merely pose an assumption. In this paper, we conduct an in-depth empirical investigation into the potential sources of performance degradation of MTL and find that task gradient conflict is one of the primary reasons for the performance degradation of tasks. Then, to address the task conflicts problem, we propose a novel gradient manipulation approach, namely MoCoGrad, which manipulates task gradients by leveraging the momentum information of the task to calibrate the gradients of conflicting tasks. In addition, we derive theoretical guarantees for the con-vergence of our proposed MoCoGrad and theoretically analyze the convergence rate of MoCoGrad. Finally, to evaluate the effectiveness of MoCoGrad, extensive experiments are conducted on six real-world datasets from different domains. Our approach yields the best performance across all tasks in all six MTL benchmarks, demonstrating the effectiveness and superiority of our method.

Aspect-based sentiment analysis aims to identify sentiment polarity of social media users toward different aspects.Most recent methods adopt the aspect-centric latent tree to connect aspects and their corresponding opinion words, thinking that would facilitate establishing the relationship between aspects and opinion words.However, these methods ignore the roles of syntax dependency relation labels and affective semantic information in determining the sentiment polarity, resulting in the wrong prediction.In this paper, we propose a novel multi-graph fusion network (MGFN) based on latent graph to leverage the richer syntax dependency relation label information and affective semantic information of words.Specifically, we construct a novel syntax-aware latent graph (SaLG) to fully leverage the syntax dependency relation label information to facilitate the learning of sentiment representations.Subsequently, a multi-graph fusion module is proposed to fuse semantic information of surrounding contexts of aspects adaptively.Furthermore, we design an affective refinement strategy to guide the MGFN to capture significant affective clues.Extensive experiments on three datasets demonstrate that our MGFN model outperforms all state-of-the-art methods and verify the effectiveness of our model.