An MRC Framework for Semantic Role Labeling (2022)

Abstract: Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks. In this paper, we propose to use the machine reading comprehension (MRC) framework to bridge this gap. We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense. The chosen predicate sense is then used to determine the semantic roles for that predicate, and these semantic roles are used to construct the query for another MRC model for argument labeling. In this way, we are able to leverage both the predicate semantics and the semantic role semantics for argument labeling. We also propose to select a subset of all the possible semantic roles for computational efficiency. Experiments show that the proposed framework achieves state-of-the-art or comparable results to previous work.

## introduction semantic role labeling (srl) aims at recognizing the predicate-argument structure of a sentence. the classic propbank-style srl includes two tasks: predicate disambiguation and argument labeling. predicate disambiguation determines the specific meaning of a predicate in a given context and argument labeling identifies the arguments of the predicate and assign them with the corresponding semantic roles, where each argument is a text span in the sentence. propbank defines two types of semantic roles for argument labeling: core roles and non-core roles @xcite . core roles * corresponding author. the stock has been beaten down for two days. there are seven core roles in propbank: a0-a5 and aa. non-core roles are modifiers, such as location (loc) and time (tmp). the specific meanings of predicates and core roles are defined in the frame files. for example, for the sentence in figure 1 , the sense id of the predicate "beaten" is "beat.02", and its three arguments span are "the stock", "down" and "for two days", whose roles are respectively a1, a2, tmp. we can get the meaning of sense label "beat.02" and roles a1 and a2 from the frame file. in traditional methods, predicate disambiguation and argument labeling are usually solved as two independent tasks. these works usually rely on feature-based methods @xcite @xcite )) for predicate disambiguation, and use span-based @xcite @xcite or biobased @xcite @xcite methods for argument labeling. these methods treat different predicate senses and argument roles as different class categories, and then solve them through classification. however, since these approaches ignore the semantic information of both predicate senses and argument roles, they are unable to establish the semantic connection be- ## conclusion in this paper, we propose an mrc-based framework for semantic role labeling. we formalize predicate disambiguation as multiple-choice reading comprehension and argument labeling as extractive reading comprehension. besides, we also propose a role prediction module to reduce the computation caused by considering all roles in the dataset for argument labeling. experiments show that our framework can effectively utilize the semantic information provided in frame files and achieve competitive results.