A Knowledge-Guided Framework for Frame Identification (2021)

Abstract: Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using onehot embeddings. However, the valuable knowledge about frames is neglected. In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. The extensive experimental results demonstrate KGFI significantly outperforms the state-of-the-art methods on two benchmark datasets.

## introduction frame identification (fi) aims to find the exact frame evoked by a target word in a given sentence. a frame represents an event scenario, and possesses frame elements (or semantic roles) that participate in the event @xcite , which is described in the framenet knowledge base @xcite grounded on the theory of frame semantics @xcite . the theory asserts that people understand the meaning of words largely by virtue of the frames which they evoke. in general, many words are polysemous and can evoke different frames in different contexts. as shown in figure 1 , the word stopped evokes the frame activity stop and the frame * corresponding author. the company stopped producing the profitable toy. activity_stop stop.v agent activity process_stop stop.v process time the fighting has stopped for more than two years . figure 1 : two annotated examples with the target word marked in bold and frame elements (semantic roles) in rounded rectangles. the target word stopped (stop.v denotes its form of lexical unit) evokes the frame activity stop and the frame process stop respectively in different contexts. here, the key to distinguish these two frames is identifying whether the subject (the compony or the fighting) of stopped is an agent or a process (see the frame definitions in process stop respectively in two sentences. it is a challenging task to distinguish the frames evoked by target words in sentences. furthermore, fi is a key step before frame semantic role labeling (fsrl) @xcite @xcite which is widely used in event recognition @xcite , machine reading comprehension @xcite , relation extraction @xcite , etc. through fi process, hundreds of role labels in framenet are reduced to a manageable small set @xcite , which can significantly improve the performance of fsrl models. thus, fi is a fundamental and critical task in nlp. fi is typically regarded as a classification task, in which class labels are frame names. in earlier studies, researchers manually construct features and then use supervised learning methods to learn classification models @xcite @xcite . these methods, however, do not take the valuable semantic information about frames into considera- @xcite , ... cease.v, halt.n, shutdown.n, stop.v,... frs inherits from: process stop inherits from: event subframe of: activity subframe of: process is inherited by: halt is inherited by: activity stop uses: eventive affecting the recent studies of fi use distributed representations of target words and their syntactic context to construct features, and construct classification models with deep neural network @xcite . these methods usually transform frame labels into one-hot representations @xcite , and then learn the embeddings of target words and frames simultaneously. however, the abundant semantic information and structure knowledge of frames contained in framenet are still neglected. knowledge of frames defined by linguists, such as frame definition, frame elements and frame-toframe relations, can enrich frame labels with rich semantic information that can potentially guide fi models to learn more unique and distinguishable representations. thus, in this paper, we propose a knowledge guided frame identification framework (kgfi) which consists of a bert-based context encoder and a frame encoder based on a specialized graph convolutional network (framegc-n). in particular, the frame encoder incorporates multiple types of frame knowledge into frame representation which guides the kgfi to jointly map target words and frames into the same embedding space. instead of predicting the frame label directly, kgfi chooses the best suitable frame evoked by the target word in a given sentence by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. in summary, our contribution is threefold: ## methodology the proposed kgfi framework consists of three components: context encoder, frame encoder and scoring module, as shown in figure 2 . specifically, context encoder is used to represent the context-aware target word into an embedding with a bert-based module, and frame encoder is used to incorporate three types of knowledge about a frame into frame embeddings. with the guidance of the knowledge about frames, two encoders jointly learn the embeddings of target words and frames. finally, a scoring module is used to calculate the similarity scores between the given target word embedding and all frames' embeddings, to identify the best frame with the highest score. ## related work researchers have made great effort to tackle the fi problem since it has been proposed in the semeval-2007 @xcite . it is generally regarded as a classification task. the best system @xcite in the semeval-2007 adopted svm to learn the classifier to identify frames with a set of features, such as target lemma, target word, and so on. semafor @xcite uti-lized a conditional model that shares features and weights across all targets, frames, and prototypes. these approaches use manually designed features and traditional machine learning methods to learn the classification models, while the class labels as supervision signals are discrete frame names. recently, distributed feature representation and models based on neural network are used to tackle fi. according to the model architecture, there are two trends of work. one is joint learning approach that converts the discrete frame labels into continuous embedding by learning the embeddings of target words and frames simultaneously. for instance, hermann-14 @xcite implemented a model that jointly maps possible frame labels and the syntax context of target words into the same latent space using the wsabie algorithm, and the syntax context was initialized with concatenating their word embeddings. simpleframeid @xcite useed the concatenation of sentbow (the average of embeddings of all the words in the sentence) to represent the context and then learns the common embedding space of context and frame labels following the line of @xcite . the other trend is to construct the classifier model using deep neural network and regard discrete frame labels as supervision signals, which is similar to those earlier work. open-sesame @xcite used a bidirectional lstm to construct the fi classifier. peng @xcite proposed a joint learning model for fi and fsrl, which adopted a multitask model structure. different from previous studies, this paper focuses on how to represent frames by incorporating frame knowledge into frame representations and enriching frame labels with semantic information. ## conclusion in this work, we propose a novel idea that leverages frame knowledge, including frame definition, frame elements and frame-to-frame relations, to improve the model performance of fi task. our proposed kgfi framework mainly consists of a bertbased context encoder and a gcn-based frame encoder which can effectively incorporate multiple types of frame knowledge in a unified framework and jointly map frames and target words into the same semantic space. extensive experimental results demonstrate that all kinds of knowledge about frames are useful for enriching the representation of frames, and the better frame representation is helpful for fi task. the experimental results also show that the proposed model achieves significantly better performance than seven state-of-the-art models across two benchmark datasets.