Definition Generation for Automatically Induced Semantic Frame (2024)

Abstract: In a semantic frame resource such as FrameNet, the definition sentence of a frame is essential for humans to understand the meaning of the frame intuitively. Recently, several attempts have been made to induce semantic frames from large corpora, but the cost of creating the definition sentences for such frames is significant. In this paper, we address a new task of generating frame definitions from a set of frame-evoking words. Specifically, given a cluster of frame-evoking words and associated exemplars induced as the same semantic frame, we utilize a large language model to generate frame definitions. We demonstrate that incorporating frame element reasoning as chain-of-thought can enhance the inclusion of correct frame elements in the generated definitions.

## introduction semantic frames are conceptual structures that describe specific types of situations or events, and framenet @xcite is one of the representative semantic frame resources. each frame in framenet comprises a frame name, a definition, a set of core and non-core frame elements (fes), a set of frame-evoking words (lexical units), and exemplars in which lexical units appear. while framenet has expanded to include multiple languages, such as spanish @xcite and japanese @xcite , it remains absent in many low-resource languages. there may also be cases where frame knowledge of different granularity or specific to a particular domain is required. accordingly, new frame resources need to be developed in some cases, but the costly annotation process hinders their construction. to address this issue, a number of efforts have been made to automate the process of building frames from large corpora. for instance, the frame induction task aims to automatically group frame-evoking frame: cutting ## frame definition generation our formulation of the semantic frame definition generation task is as follows: given a set of frameevoking words and associated exemplars clustered as one frame, our objective is to produce a cohesive natural language definition that accurately captures ## related work definition generation, also named definition modelling, was initially formulated to generate a readable word definition based on word embeddings @xcite . initially, its primary objective was to assess word embeddings' effectiveness, later evolving to generating word definitions in context. @xcite wang treat the definition generation as sequence-to-sequence task. given a target a word highlighted in the exemplar, they utilize transformer-based model to generate word definition based on contextual information @xcite . unlike definition generation task aimed at generating individual definitions for each word, the frame definition generation task involves defining semantic frame evoked by multiple words. ## conclusion and future work we introduce semantic frame definition generation tasks and employ icl to generate definitions for induced semantic frames. we explore diverse demonstration formats through experiments on framenet. results indicate that even with a limited number of ground truth demonstrations, our approach yields promising performance. our future endeavors will involve experimenting with various llms to assess their efficacy. additionally, although we currently leverage ground truth demonstrations from framenet, the clustering of induced frames may introduce noise. investigating the impact of the noise on performance will be one of our focus in the future work.