DART: Open-Domain Structured Data Record to Text Generation
Linyong Nan1 Dragomir Radev1,2 Rui Zhang3 Amrit Rau1 Abhinand Sivaprasad1 Chiachun Hsieh4 Xiangru Tang1 Aadit Vyas1 Neha Verma1 Pranav Krishna5 Yangxiaokang Liu1 Nadia Irwanto1 Jessica Pan1 Faiaz Rahman1 Ahmad Zaidi1 Murori Mutuma1 Yasin Tarabar1 Ankit Gupta1 Tao Yu1 Yi Chern Tan1 Xi Victoria Lin2 Caiming Xiong2 Richard Socher2 Nazneen Fatema Rajani2 1 Yale University 2 Salesforce Research 3 Penn State University 4 The University of Hong Kong 5 MIT {linyong.nan, Now at Facebook AI.
Abstract
We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain non-trivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and dialogue-act-based meaning representation tasks by utilizing techniques such as: tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.
中文速览
结构化数据自动转文字（Data-to-Text）是让机器把表格、知识图谱等结构化信息变成流畅自然语言的任务，但现有数据集要么只覆盖餐厅、体育等少数领域，要么用扁平的槽值对表示数据、无法刻画表格内部丰富的语义层级关系。为此，研究团队构建了 DART 这个开放域、大规模的结构化数据转文本数据集，核心创新是把维基百科表格的扁平列头转化为树状本体（tree ontology），用主-谓-宾三元组组编码列与列之间的语义依赖，同时融合 WikiSQL、WikiTableQuestions、WebNLG 和 Cleaned E2E 等多个异构数据源，最终形成超过 8.2 万条高质量的"三元组集合—句子"对。实验表明，在 DART 上训练的模型在 WebNLG 2017 上取得了新的最优成绩，而现有最强模型在 DART 上的表现却明显下滑，说明该数据集对跨域泛化提出了真实挑战。DART 的价值在于：它提供了迄今最多样的谓词覆盖和最丰富的语义结构，为训练能真正理解开放域表格并生成准确文字描述的模型奠定了数据基础。
原文 arXiv:2007.02871;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2007.02871v2