Reasoning over Entity-Action-Location Graph for Procedural Text Understanding (2021.acl-long)
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| Challenge: | Procedural text understanding aims at tracking the states and locations of entities mentioned in a paragraph. |
| Approach: | They propose a framework to model entities-entity, action, and location relations using a graph neural network. |
| Outcome: | The proposed approach outperforms strong baselines on two datasets, ProPara and Recipes. |
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| Challenge: | Using synthetic data, existing models struggle with questions that require inference. |
| Approach: | They propose a dataset and two new neural models that exploit alternative mechanisms for state prediction. |
| Outcome: | The proposed dataset improves accuracy by 19% over previous models. |
Effective Use of Transformer Networks for Entity Tracking (D19-1)
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| Challenge: | Existing pre-trained language models for entity-related tasks are not able to handle the nuances of procedural text. |
| Approach: | They propose to use pre-trained transformer networks to track entities in procedural text by restructuring input to focus on a particular entity. |
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Be Consistent! Improving Procedural Text Comprehension using Label Consistency (N19-1)
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| Challenge: | Existing systems for procedural text comprehension still struggle with this task . evaluative work shows that consistent predictions from multiple entities can improve performance . |
| Approach: | They propose a framework that leverages label consistency during training to improve prediction performance. |
| Outcome: | The proposed framework significantly improves prediction performance over previous state-of-the-art systems on a standard benchmark dataset for procedural text, ProPara. |
Understanding Procedural Text using Interactive Entity Networks (2020.emnlp-main)
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| Challenge: | Recent efforts to track multiple entities in a procedural text treat each entity separately . e.g., scientific articles, instruction books, recipes, often contain multiple entities involved . |
| Approach: | They propose a recurrent network with memory equipped cells for state tracking . they maintain different attention matrices through specific memories to model different types of entity interactions . |
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A Dataset for Tracking Entities in Open Domain Procedural Text (2020.emnlp-main)
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Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard Hovy
| Challenge: | Existing tasks require only a small set of attributes to track state changes in procedural text. |
| Approach: | They propose a task where given a procedural text as input, the task is to generate a set of state change tuples for each step. |
| Outcome: | The proposed task generates state change tuples from a set of pre-defined attributes for each step and predicts them from an open vocabulary. |
Order-Based Pre-training Strategies for Procedural Text Understanding (2024.naacl-short)
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| Challenge: | Procedural text is difficult to understand due to the changing attributes of entities in the context. |
| Approach: | They propose sequence-based pre-training methods to enhance procedural understanding in natural language processing by using ordered instructions to guide individuals through a task. |
| Outcome: | The proposed methods improve on two datasets in the datasets NPN-Cooking and ProPara domains respectively. |
Summarizing Procedural Text: Data and Approach (2022.findings-emnlp)
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| Challenge: | Procedural text summarization task is a popular task in the NLP field because of its long length and complexity. |
| Approach: | They propose a procedural text summarization task with two granularity . they propose an Entity-State Graph-based Summarizer (ESGS) which aggregates contextual information for each procedure. |
| Outcome: | The proposed model can summarize the entire procedural text or give an overview for each step or both . Experiments on two datasets confirm the proposed model's effectiveness. |
The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models (2023.findings-acl)
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| Challenge: | et al., 2023) show that entity coreference resolution is improved when events bring about changes in entities that are not reflected in text mentions. |
| Approach: | They propose to perform transformation-based entity linking prior to coreference relation identification to improve entity coreference. |
| Outcome: | The proposed model improves coreference resolution of entities mentioned under a process-oriented model of events. |
Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text (D19-1)
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| Challenge: | XPAD is a new model that predicts actions' effects and their dependencies based on background knowledge . previous work on extracting sequences of actions from text has focused on identifying why they are the way they are . |
| Approach: | They propose a new model that biases effect predictions towards those that explain more of the actions in the paragraph and are more plausible with respect to background knowledge. |
| Outcome: | The proposed model outperforms existing systems on explaining actions by predicting dependencies while maintaining the performance on the original task in ProPara. |
Constructing Procedural Graphs with Multiple Dependency Relations: A New Dataset and Baseline (2023.findings-acl)
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| Challenge: | Existing methods to structure procedural knowledge focus on representing descriptive knowledge but ignore another commonsense knowledge-Procedural Knowledge. |
| Approach: | They propose to generate flow graphs from procedural documents by extracting sequential dependency between sentences and missing two important dependencies in procedural document. |
| Outcome: | The proposed method can generate flow graphs from unstructured documents with syntactic information and discourse structures. |