Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction (2023)

Abstract: The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill. In this paper, we address the latter task of argument clustering, which aims to acquire frame element knowledge, and propose a method that applies deep metric learning. In this method, a pre-trained language model is fine-tuned to be suitable for distinguishing frame element roles through the use of frame-annotated data, and argument clustering is performed with embeddings obtained from the fine-tuned model. Experimental results on FrameNet demonstrate that our method achieves substantially better performance than existing methods.

## introduction a semantic frame is a coherent conceptual structure that describes a particular type of situation or event along with its participants and props. framenet @xcite is a representative resource, in which semantic frames define a set of frame-specific roles called frame elements @xcite . framenet comprises a list of semantic frames, sets of frame-evoking words, and collections of frameannotated examples. because manually arranging such frame resources on a large scale is labor intensive, there have been many studies on automatic induction of frame resources. most of these studies have assumed only verbs as frame-evoking words and divided the frame induction task into two sub-tasks: verb clustering, which groups verbs according to the frames that they evoke, and argument clustering, which groups arguments of verbs according to as with many natural language processing tasks, methods using contextualized embeddings such as elmo @xcite and bert @xcite have been proposed for argument clustering tasks. however, these methods have been reported to perform worse than methods based on syntactic relations @xcite . we assume that this is because vanilla bert, i.e., bert without fine-tuning, is more influenced by factors such as a whole sentence's meaning and does not emphasize information that captures differences in semantic roles. figure 1(a) shows a 2d t-sne (maaten and hinton, 2008) projection of the average bert embeddings of argument tokens in examples of the giving frame in framenet. we can see that these embeddings are not adequately clustered according to their semantic roles. hence, in this study, we propose the use of deep metric learning to fine-tune a contextual word embedding model so that instances of the same fes are placed close together while other instances are placed farther apart in the embedding space. figure 1( @xcite shows a 2d projection of the average bert embeddings of argument tokens after fine-tuning with our proposed method based on the triplet loss. we can confirm that instances of the same fes are located close to each other. this suggests that deep metric learning enables fine-tuning of bert to obtain embedding spaces that better reflect human intuition about fes. ## acquiring frame element knowledge with deep metric learning to acquire frame element knowledge for semantic frame induction, we work on argument clustering, which is the task of grouping arguments of frame-evoking words according to their roles in the evoked frame. we introduce two argument clustering methods that cluster argument instances using their contextualized word embeddings. to achieve higher performance methods, we assume the existence of frame-annotated data and propose to finetune a contextualized word embedding model using deep metric learning. ## experiment to confirm the usefulness of fine-tuning with deep metric learning, we experimented with an argument clustering task. this study focuses on argument clustering to induce fes for frame-evoking verbs. given the true frame that a verb evokes and the true positions of its argument tokens in the example sentences, we cluster only its arguments to generate role clusters. then, we merge the true frame and the role clusters to obtain the final fe clusters. ## conclusion we have addressed argument clustering for semantic frame induction. we proposed a method that uses deep metric learning to fine-tune contextualized embedding models and applied the resulting fine-tuned embeddings to perform argument clustering. we also introduced intra-frame methods that exploit the property that fes are frame-specific. experimental results showed that fine-tuned models with deep metric learning are promising and that intra-frame methods perform quite well. especially, the intra-frame method with the triplet model achieved high scores of 92.5 for pif and 88.6 for bcf. although only core frame elements are covered in this study, it would be ideal to acquire non-core frame element knowledge as well. since many noncore frame elements are shared among different frames and are likely to be easier to learn than core frame elements, our methods are expected to achieve competitive performance for non-core frame elements as well. we would like to confirm it in future work. the ultimate goal of this research is to automatically build frame knowledge resources from large text corpora. we will need to merge our method with methods that cluster verbs according to the frames that they evoke @xcite and predict the positions of argument tokens. in addition, we will consider how to apply our method to large text corpora.