A Double-Graph Based Framework for Frame Semantic Parsing (2022)

Abstract: Frame semantic parsing is a fundamental NLP task, which consists of three subtasks: frame identification, argument identification and role classification. Most previous studies tend to neglect relations between different subtasks and arguments and pay little attention to ontological frame knowledge defined in FrameNet. In this paper, we propose a Knowledge-guided Incremental semantic parser with Double-graph (KID). We first introduce Frame Knowledge Graph (FKG), a heterogeneous graph containing both frames and FEs (Frame Elements) built on the frame knowledge so that we can derive knowledge-enhanced representations for frames and FEs. Besides, we propose Frame Semantic Graph (FSG) to represent frame semantic structures extracted from the text with graph structures. In this way, we can transform frame semantic parsing into an incremental graph construction problem to strengthen interactions between subtasks and relations between arguments. Our experiments show that KID outperforms the previous state-of-the-art method by up to 1.7 F1-score on two FrameNet datasets. Our code is availavle at https://github.com/PKUnlp-icler/KID .

## introduction the frame semantic parsing task @xcite aims to extract frame semantic structures from sentences based on the lexical resource framenet @xcite . as shown in figure 1 , given a target in the sentence, frame semantic parsing consists of three subtasks: frame identification, argument identification and role classification. frame semantic parsing can also contribute to downstream nlp tasks such as machine reading comprehension @xcite , relation extraction @xcite and dialogue generation @xcite . figure 1 : given the target receive in this sentence, the frame identification is to identify the frame receiving evoked by it; the argument identification is to find the arguments (he, the book, ...) of this target; the role classification is to assign frame elements (recipient, theme, ...) as semantic roles to these arguments. framenet is an english lexical database, which defines more than one thousand hierarchicallyrelated frames to represent situations, objects or events, and nearly 10 thousand fes (frame elements) as frame-specific semantic roles with more than 100,000 annotated exemplar sentences. in addition, framenet defines ontological frame knowledge for each frame such as frame semantic relations, fe mappings and frame/fe definitions. the frame knowledge plays an important role in frame semantic parsing. most previous approaches @xcite @xcite only use exemplar sentences and ignore the ontological frame knowledge. recent researches @xcite introduce frame semantic relations and frame definitions into the subtask frame identification. different from previous work, we construct a heterogeneous graph named frame knowledge graph (fkg) based on frame knowledge to model multiple semantic relations between frames and frames, frames and fes, as well as fes and fes. furthermore, we apply fkg to all subtasks of frame semantic parsing, which can fully inject frame knowledge into frame semantic parsing. the knowledgeenhanced representations of frames and fes are learned in a unified vector space and this can also strengthen interactions between frame identification and other subtasks. most previous systems neglect interactions between subtasks, they either focus on one or two subtasks @xcite @xcite ) of frame semantic parsing or treat all subtasks independently @xcite . furthermore, in argument identification and role classification, previous approaches process each argument separately with sequence labeling strategy @xcite or spanbased graphical models @xcite . in this paper, we propose frame semantic graph (fsg) to represent frame semantic structures and treat frame semantic parsing as a process to construct this graph incrementally. with graph structure, historical decisions of parsing can guide the current decision of argument identification and role classification, which highlights interactions between subtasks and arguments. based on two graphs mentioned above, we propose our framework kid (knowledge-guided incremental semantic parser with double-graph). fkg provides a static knowledge background for encoding frames and fes while fsg represents dynamic parsing results in frame semantic parsing and highlights relations between arguments. overall, our contributions are listed as follow: we evaluate the performance of kid on two framenet datasets: fn 1.5 and fn 1.7, the results show that the kid achieves state-of-the-art on these datasets by increasing up to 1.7 points on f1-score. our extensive experiments also verify the effectiveness of these two graphs. ## ontological frame knowledge frame semantics relates linguistic semantics to encyclopedic knowledge and advocates that one cannot understand the semantic meaning of one word without essential frame knowledge related to the word @xcite . the frame knowledge of a frame contains frame/fe definitions, frame semantic relations and fe mappings. framenet defines 8 kinds of frame semantic relations such as inheritance, perspective_on and using; for any two related frames, the framenet defines fe mappings between their fes. for example, the frame receiving inherits from getting and the fe donor of receiving is mapped to the fe source of getting. each frame or fe has its own definition and may mention other fes. we propose two ways of reasoning about frame semantic parsing: inter-frame reasoning and intraframe reasoning in figure 2 . frame knowledge mentioned above can guide both ways of reasoning. the frame semantic relation between receiving and getting and fe mappings associated with it allow us to learn from the left sentence when parsing the right sentence because similar argument spans of two sentences will have related fes as their roles. the fe definitions reflect dependencies between arguments. the definition of role in frame receiving mentions theme and donor, which reflects dependencies between argument the book and argument as a gift. ## task formulation given a sentence @xmath0, . . . , w n-1 with a target span t in s, the frame semantic parsing aims to extract the frame semantic structure of t. suppose that there are k arguments of t in s: a 0 , . . . , a k-1 , subtasks can be formulated as follow: ## method kid encodes all frames and fes to knowledgeenhanced representations via frame knowledge graph encoder (section 4.1). for a sentence with a target, contextual representations of tokens are derived from the sentence encoder (section 4.2). frame semantic parsing is regarded as a process to build fsg incrementally from an initial target node to complete fsg. frame identification finds a frame evoked by the target and combines the target with its frame into the initial node of fsg (section 4.3.1). argument identification (section 4.3.2) and role classification (section 4.3.3) for each argument is based on the current snapshot of partial fsg considering all historical decisions. section 4.4 tells how frame semantic graph decoder encodes partial fsg to its representation and how it expands fsg incrementally, which is also shown in figure 4 . ## related work frame semantic parsing has caught wide attention since it was released on semeval 2007 @xcite . the task is to extract frame structures defined in framenet @xcite ) from text. from then on, a large amount of systems are applied on this task, ranging from traditional machine learning classifiers @xcite to fancy neural models like recurrent neural networks @xcite and graph neural networks @xcite . a lot of previous systems neglect interactions between subtasks and relations between arguments. they either focus on one or two subtasks @xcite @xcite of frame semantic parsing or treat all subtasks independently @xcite . @xcite propose an efficient global graphical model, so they can enumerate all possible argument spans and treat the assignment as the integer linear programming problem. later systems like @xcite ; @xcite follow this method. swayamdipta et al. (2017); bastianelli et al. (2020) use sequence @xcite integrate these two methods with a joint model. only few approaches like @xcite model interactions between subtasks, which use the encoderdecoder architecture to predict arguments and roles sequentially. however, the sequence modeling of @xcite does not consider structure information and is not good at capturing long-distance dependencies. we use graph modeling to enhance structure information and strengthen interactions between target and argument, argument and argument. only a few systems utilize linguistic knowledge in framenet. @xcite use fe mappings to share information in fes. in frame identification, @xcite encode definitions of frames and @xcite use frame identification and frame semantic relations. however, they do not utilize ontological frame knowledge in all subtasks while we construct a heterogeneous graph containing both frames and fes. besides, our model does not need extra encoders to encode definitions, which reduces parameters of the model. some systems also treat constituency parsing or other semantic parsing tasks like amr as a graph construction problem. yang and deng (2020) use gcn to encode intermediate constituency tree to generate a new action on the tree. cai and lam (2020) construct amr graphs with the transformer @xcite architecture. ## conclusion in this paper, we incorporate knowledge into frame semantic parsing by constructing frame knowledge graph. fkg provides knowledge-enhanced representations of frames and fes and can guide intra-frame and inter-frame reasoning. we also propose frame semantic graph to represent frame semantic structures. we regard frame semantic parsing as an incremental graph construction problem. the process to construct fsg is structureaware and can utilize relations between arguments. our framework knowledge-guided incremental semantic parser with double-graph (kid) achieves state-of-the-art on framenet benchmarks. however, how to utilize linguistic knowledge better is still to be resolved. future work can focus on better modeling of ontological frame knowledge, which will be useful for frame semantic parsing and transfer learning in frame semantic parsing. hongyan zhao, ru li, xiaoli li, and hongye tan. 2020. cfsre: context-aware based on framesemantics for distantly supervised relation extraction. knowledge-based systems, 210:106480.