A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks
| Autor: | Chris Brockett, Elnaz Nouri, Sudha Rao, Angela S. Lin, Asli Celikyilmaz, Debadeepta Dey, Bill Dolan |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language business.industry Computer science Recipe 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Domain (software engineering) Task (computing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Computation and Language (cs.CL) computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | ACL |
| Popis: | Many high-level procedural tasks can be decomposed into sequences of instructions that vary in their order and choice of tools. In the cooking domain, the web offers many partially-overlapping text and video recipes (i.e. procedures) that describe how to make the same dish (i.e. high-level task). Aligning instructions for the same dish across different sources can yield descriptive visual explanations that are far richer semantically than conventional textual instructions, providing commonsense insight into how real-world procedures are structured. Learning to align these different instruction sets is challenging because: a) different recipes vary in their order of instructions and use of ingredients; and b) video instructions can be noisy and tend to contain far more information than text instructions. To address these challenges, we first use an unsupervised alignment algorithm that learns pairwise alignments between instructions of different recipes for the same dish. We then use a graph algorithm to derive a joint alignment between multiple text and multiple video recipes for the same dish. We release the Microsoft Research Multimodal Aligned Recipe Corpus containing 150K pairwise alignments between recipes across 4,262 dishes with rich commonsense information. This paper has been accepted to be published at ACL 2020 |
| Databáze: | OpenAIRE |
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