Frame identification, which is finding the exact evoked frame for a target word in a given sentence, is a fundamental and crucial prerequisite for frame semantic parsing. It is generally seen as a classification task for target words, whose contextual representations are usually obtained using a neural network like BERT as an encoder, and enriched with a joint learning model or the knowledge of FrameNet. However, the distinction at a fine-grained level, such as the delicate differences in the information of syntax and PropBank roles caused by different parts-of-speech (POS) of target words, is neglected. We propose a Multiple POS Dependency-aware Mixture of Experts(MPDaMoE) network that integrates five types of information, consisting of the syntactic information of target words whose POS are nominal, adjectival, adverbial, or prepositional, and the PropBank role information of target words whose POS are only verbal.To better learn such information, a Mixture of Experts network is employed, in which every expert is a Graph Convolutional Network, to incorporate the different dependency information of target words. Our model outperforms state-of-the-art models in experiments on two benchmark datasets, which shows its effectiveness.
Frame-semantic Parsing (FSP) is a challenging and critical task in Natural Language Processing (NLP). Most of the existing studies decompose the FSP task into frame identification (FI) and frame semantic role labeling (FSRL) subtasks, and adopt a pipeline model architecture that clearly causes error propagation problem. On the other hand, recent jointly learning models aim to address the above problem and generally treat FSP as a span-level structured prediction task, which unfortunately leads to cascading error propagation problem between roles and less efficient solutions due to huge search space of roles. To address these problems, we reformulate the FSRL task into a target-aware relation classification task , and propose a novel and lightweight jointly learning framework that simultaneously processes three subtasks of FSP, including frame identification, argument identification and role classification. The novel task formulation and jointly learning with interaction mechanisms among subtasks can help improve the overall system performance, and reduce the search space and time complexity, compared with existing methods. Extensive experimental results demonstrate that our proposed model significantly outperforms ten state-of-the-art models in terms of F1 score across two benchmark datasets.
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