Jinwoo Park, Hosoo Shin, Dahee Jeong, Junyeong Kim
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引用次数: 0
摘要
句子嵌入是一种以向量形式表示句子含义的技术,在问题解答、情感分析和信息检索等各种自然语言处理任务中发挥着至关重要的作用。因此,理解句子的含义和结构至关重要。我们提出了一种利用抽象意义表示(AMR)解析和强化学习来提高句子嵌入性能的新方法。我们使用 AMRBART(一种 AMR 解析器)生成句子嵌入,并在问题解答(QA)任务中对其进行评估。在此过程中,我们使用带前瞻性的加权漫步得分(Weighted Walks with Lookahead score)来衡量两个句子的 AMR 图之间的相似性,并采用强化学习算法深度确定性策略梯度算法(Deep Deterministic Policy Gradient algorithm)来提高该得分。通过将 AMR 句法分析和强化学习集成到句子嵌入生成过程中,我们可以更准确地理解自然语言句子。
Improving the Representation of Sentences with Reinforcement Learning and AMR graph
Sentence Embedding is a technique that represents the meaning of sentences in vector form, playing a crucial role in various natural language processing tasks such as question-answering, sentiment analysis, and information retrieval. Therefore, understanding the meaning and structure of sentences is essential. We propose a novel approach to improve the performance of Sentence Embedding by utilizing Abstract Meaning Representation(AMR) parsing and reinforcement learning. We generate Sentence Embeddings using AMRBART, a type of AMR parser, and evaluate them in Question Answering (QA) tasks. In this process, we measure the similarity between the AMR graphs of two sentences using the Weighted Walks with Lookahead score and employ the Deep Deterministic Policy Gradient algorithm, a reinforcement learning algorithm, to enhance this score. By integrating AMR syntactic analysis and reinforcement learning into the Sentence Embedding generation process, we enable a more accurate understanding of natural language sentences.