论文标题
FormLM：通过建模语义和结构信息，推荐在线形式的创建思想
FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information
论文作者
论文摘要
在线表格被广泛用于从人类那里收集数据，并拥有数十亿个市场。许多软件产品提供在线服务，以创建半结构化表单，其中通过预定义的结构组织了问题和描述。但是，形式的设计和创建过程仍然乏味，需要专家知识。为了协助表格设计师，在这项工作中，我们提出了Formlm，以建模在线形式（通过增强具有形式的结构信息的预训练的语言模型）并建议形式创建想法（包括问题 /选项建议和块类型建议）。对于模型培训和评估，我们收集了第一个带有62K在线表格的公开在线表格数据集。实验结果表明，在所有任务上，FormLM在所有任务上的表现都显着胜过通用语言模型，在问题建议方面的提高了4.71，分别在Rouge-1和Macro-F1方面提高了10.6。
Online forms are widely used to collect data from human and have a multi-billion market. Many software products provide online services for creating semi-structured forms where questions and descriptions are organized by pre-defined structures. However, the design and creation process of forms is still tedious and requires expert knowledge. To assist form designers, in this work we present FormLM to model online forms (by enhancing pre-trained language model with form structural information) and recommend form creation ideas (including question / options recommendations and block type suggestion). For model training and evaluation, we collect the first public online form dataset with 62K online forms. Experiment results show that FormLM significantly outperforms general-purpose language models on all tasks, with an improvement by 4.71 on Question Recommendation and 10.6 on Block Type Suggestion in terms of ROUGE-1 and Macro-F1, respectively.
