A Graph-Based Neural Model for End-to-End Frame Semantic Parsing (2021)

Abstract: Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem. In this work, we propose an end-to-end neural model to tackle the task jointly. Concretely, we exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. All predicates and roles are treated as graph nodes, and their relations are taken as graph edges. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.

## introduction frame semantic parsing @xcite aims to analyze all sentential predicates as well as their framenet roles as a whole, which has received great interest recently. this task can be helpful for a number of tasks, including information extraction @xcite , question answering @xcite , machine translation @xcite and others @xcite @xcite . figure 1 shows an example, where all predicates as well as their semantic frame and roles in the sentence are depicted. previous studies @xcite @xcite usually divide the task into three subtasks, including target identification, frame classification and semantic role labeling (srl), respectively. by performing the three subtasks sequentially, the whole frame semantic parsing can be accomplished. the majority of figure 1 : an example involving frame semantic structures, taken from the framenet @xcite . frame-evoking predicates are highlighted in the sentence, and corresponding frames are shown in colored blocks below. the frame-specific roles are underlined with their frames in the same row. the above formalization has two weaknesses. first, the individual modeling of the three subtasks is inefficient to utilize the relationship among them. apparently, the earlier subtasks can not exploit the information from their future subtasks. second, the pipeline strategy can suffer from the error propagation problem, where the errors occurring in the previous subtasks can influence the later subtasks as well. to address the two weaknesses, end-to-end modeling is one promising alternative, which has been widely adopted in natural language processing (nlp) @xcite @xcite @xcite . in this work, we propose a novel graph-based model to tackle frame semantic parsing in an endto-end way, using a single model to perform the three subtasks jointly. we organize all predicates and their framenet semantic by a graph, and then design an end-to-end neural model to construct the graph incrementally. an encoder-decoder model is presented to achieve the graph building goal, where the encoder is equipped with contextualized bert representation @xcite , and the decoder includes node generation and edge building sequentially. our final model is elegant and easy to understand as a whole. we conduct experiments on two benchmark datasets to evaluate the effectiveness of our proposed model. first, we study our graph-based framework in two settings, the end-to-end scenario and the pipeline manner, where the node building and edge building are trained separately. results show that end-to-end modeling is much better. besides, we also compare our model with several other pipelines, where the similar findings can be observed. second, we compare our graph-based framework with previous methods by the three subtasks individually, finding that the graph-based architecture is highly competitive. we can obtain the best performance in the literature, leading to a new state-of-the-art result. further, we conduct extensive analyses to understand our method in depth. in summary, we make the following two major contributions in this work: ## related work frame-semantic parsing frame-semantic parsing has been received great interest since being released as an evaluation task of semeval 2007 @xcite . the task attempts to predict semantic frame structures defined in framenet @xcite which are composed of frame-evoking predicates, their corresponding frames and semantic roles. most of the previous works @xcite @xcite focus on a pipeline framework to solve the task, training target identification, frame classification and semantic role labeling models separately. in this work, to the best of our knowledge, we present the first end-to-end model to handle the task jointly. among the three subtasks of frame semantic parsing, semantic role labeling has been researched most extensively @xcite @xcite @xcite . it is also highly related to the propbank-style semantic role labeling @xcite as while with only differences in the frame definition. thus the models between the two types of semantic role labeling can be mutually borrowed. there are several end-to-end propbank-style semantic role labeling models as well @xcite @xcite . however, these models are difficult to be applied directly for frame semantic parsing due to the additional frame classification as well as the discontinuous predicates. in this work, we present a totally-different graph construction style model to solve end-to-end frame semantic parsing elegantly. graph-based methods recently, graph-based methods have been widely used in a range of other tasks, such as dependency parsing @xcite @xcite , amr parsing @xcite @xcite and relation extraction @xcite @xcite . in this work, we aims for frame semantic parsing, organizing the three included subtasks by a well designed graph, converting it into graph-based parsing task naturally. ## conclusion in this paper, we proposed a novel graph-based model to address the end-to-end frame semantic parsing task. the full frame semantic parsing result of one sentence is organized as a graph, and then we suggest an end-to-end neural model for the graph building. our model first encodes the input sentence for span representations with bert, and then constructs the graph nodes and edges incrementally. to demonstrate the effectiveness of our method, we derived several pipeline methods and used them to conduct the experiments for comparisons. experimental results showed that our graph-based model achieved significantly better performance than various pipeline methods. in addition, in order to compare our models with previous studies in the literature, we conducted experiments in the scenarios of the individual subtasks. the results showed that our proposed models are highly competitive.