| Challenge: | a recipe explains step by step how to cook a dish, but recipes differ in which cooking actions they describe explicitly, how they describe them, and in which order. |
| Approach: | They propose a recipe corpus which annotates cooking steps in recipes at sentence level . they train a neural model to predict recipes on ARA and model it for automatic understanding . |
| Outcome: | The proposed model can predict recipes with fine-grained structural information . it shows that recipes can be explained in different ways, or not at all . |
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English Recipe Flow Graph Corpus (2020.lrec-1)
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| Challenge: | Annotated corpus of English cooking recipe procedures with domain-specific linguistic and semantic structure. |
| Approach: | They annotate a corpus of English cooking recipe procedures with domain-specific linguistic and semantic structure and then use a flow graph to represent the sequence of steps. |
| Outcome: | The proposed methods achieve 71.1 to 87.5 F1 in the cooking domain and a flow graph achieves similarity to those used in Japanese recipes. |
Understanding the Cooking Process with English Recipe Text (2023.findings-acl)
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| Challenge: | Existing approaches to translate recipes into a flow graph have performance problems . authors propose a framework to construct a graph from recipe text . |
| Approach: | They propose a framework that can be used to translate recipes into a flow graph representation. |
| Outcome: | The proposed framework can predict the edge label and achieve the overall F1 score of 92.2 on the English recipe flow graph corpus. |
A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks (2020.acl-main)
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| Challenge: | a web-based algorithm can be used to align instructions for different tasks . video instructions can be noisy and contain far more information than textual instructions. |
| Approach: | They propose an algorithm that learns pairwise alignments between different recipes . they then use a graph algorithm to derive a joint alignment between multiple video and text recipes based on the same recipe. |
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Recipe Instruction Semantics Corpus (RISeC): Resolving Semantic Structure and Zero Anaphora in Recipes (2020.aacl-main)
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| Challenge: | Existing approaches to understanding recipe instructions make assumptions that are domain specific. |
| Approach: | They propose a new dataset for information extraction on recipes . they avoid a priori pre-defining domain-specific predicates to recognize . instead, they focus on basic understanding of the expressed semantics . |
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Assistive Recipe Editing through Critiquing (2023.eacl-main)
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| Challenge: | Existing methods for generating recipes that satisfy dietary restrictions are inconsistent or incoherent and paired datasets are not available at scale. |
| Approach: | They propose to build a hierarchical denoising auto-encoder that edits recipes given ingredient-level critiques by interacting with the predicted ingredients. |
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Losses that Cook: Topological Optimal Transport for Structured Recipe Generation (2026.findings-acl)
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| Challenge: | Existing work on cooking recipes relies on cross-entropy, but it does not address holistic composition of ingredient sets and numerical aspects of recipes. |
| Approach: | They propose a topological loss that represents ingredient lists as point clouds in embedding space . they show that the Dice loss excels in time/temperature precision . |
| Outcome: | The proposed model improves ingredient- and action-level metrics while preserving time/temperature precision. |
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text (2022.findings-acl)
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| Challenge: | Current research on anaphora resolution is mostly based on declarative text, such as chemical patents or instruction manuals. |
| Approach: | They propose a framework for anaphora annotation for the chemical domain for modeling anamorphic phenomena in recipes and chemical patents. |
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Building Hierarchically Disentangled Language Models for Text Generation with Named Entities (2020.coling-main)
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| Challenge: | Named entities pose a unique challenge to traditional methods of language modeling. |
| Approach: | They propose a Hierarchically Disentangled Model for named entities in cooking recipes using a dataset from several publicly available online sources. |
| Outcome: | The proposed model is based on 158,473 cooking recipes from public sources. |
PizzaCommonSense: A Dataset for Commonsense Reasoning about Intermediate Steps in Cooking Recipes (2024.findings-emnlp)
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| Challenge: | Understanding procedural texts is essential for enabling machines to follow instructions and reason about tasks. |
| Approach: | They propose a corpus of cooking recipes enriched with descriptions of intermediate steps . they propose enabling machines to follow instructions and reason about tasks . |
| Outcome: | The proposed model achieves only 26% human-evaluated preference for generations . pizzaCommonsense is a benchmark for the reasoning capabilities of large language models . |
End-to-end Parsing of Procedural Text into Flow Graphs (2024.lrec-main)
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| Challenge: | Existing flow graph parsers lack sufficient annotated data to train them . a lack of annotation can cause costly training, and poor flow graph training results in a large improvement. |
| Approach: | They propose a multi-task framework that performs tagging and graph generation simultaneously . they take advantage of the abundance of unlabelled recipes and generate noisy silver annotations . |
| Outcome: | The proposed model can unify the input representation and use compact encoders, resulting in small models with significantly fewer parameters than existing models. |