@inproceedings{shao-etal-2022-formlm,
title = "{F}orm{LM}: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information",
author = "Shao, Yijia and
Zhou, Mengyu and
Zhong, Yifan and
Wu, Tao and
Han, Hongwei and
Han, Shi and
Huang, Gideon and
Zhang, Dongmei",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.557",
doi = "10.18653/v1/2022.emnlp-main.557",
pages = "8133--8149",
abstract = "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 predefined 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.",
}
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<abstract>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 predefined 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.</abstract>
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%0 Conference Proceedings
%T FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information
%A Shao, Yijia
%A Zhou, Mengyu
%A Zhong, Yifan
%A Wu, Tao
%A Han, Hongwei
%A Han, Shi
%A Huang, Gideon
%A Zhang, Dongmei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F shao-etal-2022-formlm
%X 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 predefined 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.
%R 10.18653/v1/2022.emnlp-main.557
%U https://aclanthology.org/2022.emnlp-main.557
%U https://doi.org/10.18653/v1/2022.emnlp-main.557
%P 8133-8149
Markdown (Informal)
[FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information](https://aclanthology.org/2022.emnlp-main.557) (Shao et al., EMNLP 2022)
ACL
- Yijia Shao, Mengyu Zhou, Yifan Zhong, Tao Wu, Hongwei Han, Shi Han, Gideon Huang, and Dongmei Zhang. 2022. FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8133–8149, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.