程序性教学文档的稀疏图表示
Sparse Graph Representations for Procedural Instructional Documents
AI总结：
针对程序性教学文档的相似度计算，提出将文档对表示为融合顺序信息的稀疏有向联合概念交互图，采用超基因组排序和哈密顿路径算法替换边，并配合孪生编码器与图卷积网络，在顺序数据集上超越基线十分。
摘要：
文档相似度计算是自然语言处理多个领域中的关键任务，广泛应用于去重、匹配和推荐。传统文档相似度计算方法包括学习文档表示，并在嵌入上采用相似度或距离函数。然而，成对的相似性和差异性无法通过单个表示有效捕获。图表示方法如联合概念交互图（JCIG）将一对文档表示为联合无向加权图，JCIG提供了文档对的可解释图表示。但JCIG是无向的，未考虑文档中句子的顺序流。我们提出两种方法，通过将文档对表示为包含顺序信息的定向稀疏JCIG来建模文档相似度。我们提出了受超基因组排序和哈密顿路径启发的两种算法，将无向边替换为有向边。我们的方法还将图稀疏化，从JCIG最坏情况下的O(n^2)条边降至O(n)条边。我们展示了由孪生编码器和图卷积网络组成的稀疏有向图模型架构，在不包含顺序信息的数据集上达到与基线相当的结果，并在包含顺序信息的教学文档数据集上比基线高出十个百分点。
英文摘要：
Computation of document similarity is a critical task in various NLP domains that has applications in deduplication, matching, and recommendation. Traditional approaches for document similarity computation include learning representations of documents and employing a similarity or a distance function over the embeddings. However, pairwise similarities and differences are not efficiently captured by individual representations. Graph representations such as Joint Concept Interaction Graph (JCIG) represent a pair of documents as a joint undirected weighted graph. JCIGs facilitate an interpretable representation of document pairs as a graph. However, JCIGs are undirected, and don't consider the sequential flow of sentences in documents. We propose two approaches to model document similarity by representing document pairs as a directed and sparse JCIG that incorporates sequential information. We propose two algorithms inspired by Supergenome Sorting and Hamiltonian Path that replace the undirected edges with directed edges. Our approach also sparsifies the graph to $O(n)$ edges from JCIG's worst case of $O(n^2)$. We show that our sparse directed graph model architecture consisting of a Siamese encoder and GCN achieves comparable results to the baseline on datasets not containing sequential information and beats the baseline by ten points on an instructional documents dataset containing sequential information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
