| 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. |
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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. |
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. |
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (P18-1)
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| Challenge: | Existing methods for semantic parsing are difficult to design and learn, especially in wideopen domains. |
| Approach: | They propose a neural semantic parsing approach which models semantic par- sing as an end-to-end semantic graph generation process. |
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Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding (2025.naacl-long)
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| Challenge: | Flowcharts are typically presented as images, driving the trend of using vision-language models for end-to-end flowchart understanding. |
| Approach: | They propose a vision-language model (VLM) that generates textual representations from flowchart images and a textual Reasoner that performs question-answering based on the text representations. |
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TOD-Flow: Modeling the Structure of Task-Oriented Dialogues (2023.emnlp-main)
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Sungryull Sohn, Yiwei Lyu, Anthony Liu, Lajanugen Logeswaran, Dong-Ki Kim, Dongsub Shim, Honglak Lee
| Challenge: | Recent advances in task-oriented dialogue systems have limitations regarding transparency and controllability. |
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End-to-End Graph-Based TAG Parsing with Neural Networks (N18-1)
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| Challenge: | Using BiLSTMs, highway connections, and character-level CNNs, we propose a graph-based Tree Adjoining Grammar (TAG) parser. |
| Approach: | They propose a graph-based Tree Adjoining Grammar parser that uses BiLSTMs, highway connections, and character-level CNNs. |
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A Lightweight Modeling Middleware for Corpus Processing (L18-1)
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| Challenge: | Present-day empirical research in computational or theoretical linguistics has richly annotated and diverse corpus resources. |
| Approach: | They propose a framework for modeling arbitrary multi-modal corpus resources in a unified form for processing tools. |
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A Graph-Based Neural Model for End-to-End Frame Semantic Parsing (2021.emnlp-main)
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| Challenge: | Existing studies focus on frame semantic parsing as a graph construction problem. |
| Approach: | They propose an end-to-end neural model to tackle frame semantic parsing jointly. |
| Outcome: | The proposed model is highly competitive and performs better than pipeline models on two benchmark datasets. |
Process-Level Representation of Scientific Protocols with Interactive Annotation (2021.eacl-main)
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| Challenge: | Existing efforts to automate wet lab workflows are focusing on graph-prediction models that capture both concrete, exact quantities ("30 minutes") and vague instructions ("swirl") |
| Approach: | They manually annotate PEGs in a corpus of complex lab protocols with a novel interactive textual simulator that keeps track of entity traits and semantic constraints during annotation. |
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Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13) (D19-53)
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| Challenge: | TextGraphs is a workshop on graph-based methods for natural language processing . the workshop is being organized in conjunction with the 9th International Joint Conference on Natural Language Processing . |
| Approach: | TextGraphs is the 13th edition of the Workshop on Graph-Based Methods for Natural Language Processing . the workshop promotes synergy between GT and natural language processing . |
| Outcome: | the 2013 edition of TextGraphs is being held in conjunction with the 9th International Joint Conference on Natural Language Processing in Hong Kong. |