MG-SIN: Multigraph Sparse Interaction Network for Multitask Stance Detection.

Stance detection on social media aims to identify if an individual is in support of or against a specific target. Most existing stance detection approaches primarily rely on modeling the contextual semantic information in sentences and neglect to explore the pragmatics dependency information of words, thus degrading performance. Although several single-task learning methods have been proposed to capture richer semantic representation information, they still suffer from semantic sparsity problems caused by short texts on social media. This article proposes a novel multigraph sparse interaction network (MG-SIN) by using multitask learning (MTL) to identify the stances and classify the sentiment polarities of tweets simultaneously. Our basic idea is to explore the pragmatics dependency relationship between tasks at the word level by constructing two types of heterogeneous graphs, including task-specific and task-related graphs (tr-graphs), to boost the learning of task-specific representations. A graph-aware module is proposed to adaptively facilitate information sharing between tasks via a novel sparse interaction mechanism among heterogeneous graphs. Through experiments on two real-world datasets, compared with the state-of-the-art baselines, the extensive results exhibit that MG-SIN achieves competitive improvements of up to 2.1% and 2.42% for the stance detection task, and 5.26% and 3.93% for the sentiment analysis task, respectively.

A Model-Agnostic Approach to Mitigate Gradient Interference for Multi-Task Learning.

Multitask learning (MTL) is a powerful technique for jointly learning multiple tasks. However, it is difficult to achieve a tradeoff between tasks during iterative training, as some tasks may compete with each other. Existing methods manually design specific network models to mitigate task conflicts, but they require considerable manual effort and prior knowledge about task relationships to tune the model so as to obtain the best performance for each task. Moreover, few works have offered formal descriptions of task conflicts and theoretical explanations for the cause of task conflict problems. In this article, we provide a formal description of task conflicts that are caused by the gradient interference problem of tasks. To alleviate this issue, we propose a novel model-agnostic approach to mitigate gradient interference (MAMG) by designing a gradient clipping rule that directly modifies the interfering components on the gradient interfering direction. Specifically, MAMG is model-agnostic and thus it can be applied to a large number of multitask models. We also theoretically prove the convergence of MAMG and its superiority to existing MTL methods. We evaluate our method on a variety of real-world large datasets, and extensive experimental results confirm that MAMG can outperform some state-of-the-art algorithms on different types of tasks and can be easily applied to various methods.