Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals

Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals

Te-Lin Wu, Alex Spangher, Pegah Alipoormolabashi, Marjorie Freedman, Ralph Weischedel, and Nanyun Peng, in Proceedings of the Conference of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), 2022.

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Abstract

The ability to sequence unordered events is evidence of comprehension and reasoning about real world tasks/procedures, and is essential for applications such as task planning and multi-source instruction summarization. It often requires thorough understanding of temporal common sense and multimodal information, since these procedures are often conveyed by a combination of texts and images. While humans are capable of reasoning about and sequencing unordered procedural instructions, the extent to which the current machine learning methods possess such a capability is still an open question. In this work, we benchmark models’ capability of reasoning over and sequencing unordered multimodal instructions by curating datasets from online instructional manuals and collecting comprehensive human annotations. We find current state-of-the-art models not only perform significantly worse than humans but also seem incapable of efficiently utilizing multimodal information. To improve machines’ performance on multimodal event sequencing, we propose sequence-aware pretraining techniques exploiting the sequential alignment properties of both texts and images, resulting in >5% improvements on perfect match ratio.

Bib Entry

@inproceedings{wu2022procedural,
  title = {Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals},
  author = {Wu, Te-Lin and Spangher, Alex and Alipoormolabashi, Pegah and Freedman, Marjorie and Weischedel, Ralph and Peng, Nanyun},
  booktitle = {Proceedings of the Conference of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)},
  year = {2022}
}

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   The ability to sequence unordered events is evidence of comprehension and reasoning about real world tasks/procedures, and is essential for applications such as task planning and multi-source instruction summarization. It often requires thorough understanding of temporal common sense and multimodal information, since these procedures are often conveyed by a combination of texts and images. While humans are capable of reasoning about and sequencing unordered procedural instructions,  the extent to which the current machine learning methods possess such a capability is still an open question. In this work, we benchmark models’ capability of reasoning over and sequencing unordered multimodal instructions by curating datasets from online instructional manuals and collecting comprehensive human annotations. We find current state-of-the-art models not only perform significantly worse than humans but also seem incapable of efficiently utilizing  multimodal information. To improve machines’ performance on multimodal event sequencing, we propose sequence-aware pretraining techniques exploiting the sequential alignment properties of both texts and images, resulting in >5% improvements on perfect match ratio.

   @inproceedings{wu2022procedural,
     title = {Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals},
     author = {Wu, Te-Lin and Spangher, Alex and Alipoormolabashi, Pegah and Freedman, Marjorie and Weischedel, Ralph and Peng, Nanyun},
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     year = {2022}
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