mForms : Multimodal Form Filling with Question Answering

Larry Heck, Simon Heck, Anirudh S. Sundar

Abstract
This paper presents a new approach to form-filling by reformulating the task as multimodal natural language Question Answering (QA). The reformulation is achieved by first translating the elements on the GUI form (text fields, buttons, icons, etc.) to natural language questions, where these questions capture the element’s multimodal semantics. After a match is determined between the form element (Question) and the user utterance (Answer), the form element is filled through a pre-trained extractive QA system. By leveraging pre-trained QA models and not requiring form-specific training, this approach to form-filling is zero-shot. The paper also presents an approach to further refine the form-filling by using multi-task training to incorporate a potentially large number of successive tasks. Finally, the paper introduces a multimodal natural language form-filling dataset Multimodal Forms (mForms), as well as a multimodal extension of the popular ATIS dataset to support future research and experimentation. Results show the new approach not only maintains robust accuracy for sparse training conditions but achieves state-of-the-art F1 of 0.97 on ATIS with approximately 1/10th the training data.
Anthology ID:
2024.lrec-main.984
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
11262–11271
Language:
URL:
https://aclanthology.org/2024.lrec-main.984
DOI:
Bibkey:
Cite (ACL):
Larry Heck, Simon Heck, and Anirudh S. Sundar. 2024. mForms : Multimodal Form Filling with Question Answering. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11262–11271, Torino, Italia. ELRA and ICCL.
Cite (Informal):
mForms : Multimodal Form Filling with Question Answering (Heck et al., LREC-COLING 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-3/2024.lrec-main.984.pdf
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