FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information
This addresses the tedious and expert-dependent process of designing online forms, which have a multi-billion market, but the approach is incremental as it builds on existing language models.
The paper tackles the problem of assisting form designers by proposing FormLM, a model that enhances pre-trained language models with structural information to recommend creation ideas for online forms, resulting in improvements of 4.71 on Question Recommendation and 10.6 on Block Type Suggestion.
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.