Exploiting Definitions for Frame Identification (2021)

Abstract: Frame identification is one of the key challenges for frame-semantic parsing. The goal of this task is to determine which frame best captures the meaning of a target word or phrase in a sentence. We present a new model for frame identification that uses a pre-trained transformer model to generate representations for frames and lexical units (senses) using their formal definitions in FrameNet. Our frame identification model assesses the suitability of a frame for a target word in a sentence based on the semantic coherence of their meanings. We evaluate our model on three data sets and show that it consistently achieves better performance than previous systems.

## introduction research on frame semantics has grown within the fields of natural language processing and cognitive science since the 1970s as the study of how we associate words and phrases with cognitive structures called frames, which characterize a small abstract scene or situation @xcite . the berkeley framenet project @xcite provides an online lexical database for frame semantics together with a corpus of annotated documents. frame semantic parsing is the task of automatically extracting frame semantic structures from sentences. the process typically consists of three steps: target identification, which identifies frame-evoking predicates in the sentence; frame identification, which identifies the evoked frame for each target; and argument identification, which identifies arguments of a frame and labels them with semantic roles (frame elements). in this work, we focus on the frame identification problem. framenet 1.7 contains more than 13,000 lexical units (a word lemma with a sense), each associated with a semantic frame. a polysemous word is associated with multiple lexical units (one for each sense), and is therefore linked to multiple frames. the frame identification task requires a system to identify the most relevant frame for a target word or phrase based on its sentence context. here is an example: the pandemic has sparked a lot of problems for the economy. given the target word sparked, the goal is to determine which frame should be triggered. the word lemma spark has two senses in framenet: "with obj. ignite" and "provide the stimulus for". the former sense is associated with the setting fire frame and the latter one is associated with the cause to start frame. the setting fire frame is defined as "this frame describes the creation of a flame...", and the cause to start frame is defined as "a cause, animate or inanimate, causes a process, the effect, to begin". so cause to start is the correct frame for this sentence. previous work has shown the success of using feature engineering with linear classification models @xcite and discriminative probabilistic models @xcite , which were later improved by applying distributed word representations and deep neural network models @xcite . syntactic information, typically dependency paths, has consistently played an important role in frame identification @xcite . our work is motivated by the rich lexicographic information about frames and lexical units provided by the framenet database, which has not been fully utilized for the frame identification task. recent advances in large pre-trained transformer models @xcite have demonstrated the ability to capture semantic meaning in dictionary definitions for the related problem of word sense disambiguation @xcite . frame definition lexical unit definition [cls] [sep] [sep] sentence lexical unit definition frame definition ... ℒ bert linear ... [target ] [cls] [sep] [sep] sentence [target ] our model uses the definitions of frames and lexical units in framenet as a source of knowledge to help assess the semantic coherence between the target word and candidate frames. specifically, we utilize the contextual embeddings produced by the bert @xcite model to determine if a candidate lexical unit and frame express the same meaning as the target word in the given context. our model achieves state-of-the-art performance on two framenet datasets and a framenetannotated dataset based on yahoo! answers. our code is open-source and available online foot_0 . ## related work there has been considerable work on the frame identification problem with respect to framenet, especially since the semeval 2007 shared task @xcite . @xcite used a svm classifier to disambiguate frames with hand-crafted features. @xcite applied feature-based discriminative probabilistic (log-linear) models for frame identification. @xcite presented a method using distributed representations of predicates and their syntactic context by mapping input representations and frame representations to a common latent space using the wsabie algorithm @xcite . hartmann et al. (2017) @xcite integrated a bidirectional lstm neural network and a relational network to jointly decode frames. more recently, @xcite brought in multimodal representations grounded in images to improve frame identification. @xcite proposed a joint inference formulation that learns semantic parsers from multiple datasets. in contrast to the previous models, our model does not rely on syntactic features. we assess semantic coherence directly from the input sentence and definitions in framenet. another line of related work is learning embeddings from dictionary definitions. it has been shown that neural networks can extract semantic information from dictionary definitions @xcite . recent work in word sense disambiguation @xcite has demonstrated that providing pre-trained language models with sense definitions (glosses) can be effective. yong and torrent (2020) also used the sense definitions of lexical units for their research on frame induction. our model adopts a similar architecture as @xcite , but we focus on the frame identification task and we explore the use of both lexical unit and frame definitions for this task. ## method given a sentence and a target word or phrase, the frame identification task assigns the most relevant frame to the target according to the sentence context. figure 1 shows the framework of our model called fido (frame identification with definitions). our system takes the sentence and the definitions of associated lexical units (senses) and their frames as input to the bert model, as indicated by the green blocks. each green block represents the target word in the sentence, one of its senses, and that sense's associated frame in framenet. then we use the output vectors to produce a probability distribution over all of the candidate frames. we select the frame with the maximum probability as the answer. ## conclusion we tackled the frame identification problem by assessing the semantic coherence between the meaning of a target word in a sentence, and a candidate frame. our model exploits the frame and lexical unit definitions provided by framenet and a pretrained transformer model to generate semantic representations. the experiments show that this model achieves better performance than previous systems on two versions of framenet data and the yags dataset. our work has demonstrated that a relatively simple architecture that brings together pre-trained language models with frame and sense definitions can produce a highly effective system for frame identification.