FrameEOL：使用因果语言模型的语义框架归纳
FrameEOL: Semantic Frame Induction using Causal Language Models
AI总结：
本文提出基于因果语言模型的语义框架归纳方法FrameEOL，利用提示、上下文学习和深度度量学习获取框架嵌入并聚类，在英日FrameNet数据集上优于现有方法。
摘要：
语义框架归纳是根据框架激发词所激发的语义框架对其进行聚类的任务。近年来，利用通过BERT等掩码语言模型（MLM）获得的框架激发词嵌入，实现了高性能的语义框架归纳。尽管GPT和Llama系列等因果语言模型（CLM）在广泛的自然语言理解任务中取得了成功，并且能够像理解框架一样进行对话，但它们尚未应用于语义框架归纳。我们提出了一种基于CLM的语义框架归纳新方法。具体而言，我们引入了FrameEOL，这是一种基于提示的方法，用于获取框架嵌入，该方法输出一个框架名称作为表示给定情境的标签。为了获得更适合框架归纳的嵌入，我们利用了上下文学习（ICL）和深度度量学习（DML）。随后通过对生成的嵌入进行聚类来执行框架归纳。在英文和日文FrameNet数据集上的实验结果表明，所提出的方法优于现有的框架归纳方法。特别是对于缺乏丰富框架资源的日语，仅使用5个ICL示例的基于CLM的方法，取得了与使用DML微调的基于MLM的方法相当的性能。
英文摘要：
Semantic frame induction is the task of clustering frame-evoking words according to the semantic frames they evoke. In recent years, leveraging embeddings of frame-evoking words that are obtained using masked language models (MLMs) such as BERT has led to high-performance semantic frame induction. Although causal language models (CLMs) such as the GPT and Llama series succeed in a wide range of language comprehension tasks and can engage in dialogue as if they understood frames, they have not yet been applied to semantic frame induction. We propose a new method for semantic frame induction based on CLMs. Specifically, we introduce FrameEOL, a prompt-based method for obtaining Frame Embeddings that outputs One frame-name as a Label representing the given situation. To obtain embeddings more suitable for frame induction, we leverage in-context learning (ICL) and deep metric learning (DML). Frame induction is then performed by clustering the resulting embeddings. Experimental results on the English and Japanese FrameNet datasets demonstrate that the proposed methods outperform existing frame induction methods. In particular, for Japanese, which lacks extensive frame resources, the CLM-based method using only 5 ICL examples achieved comparable performance to the MLM-based method fine-tuned with DML.
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