Papers with ProPara
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training (2022.findings-emnlp)
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| Challenge: | Existing approaches to language-based environment manipulation are difficult to generalize across environments. |
| Approach: | They propose a general framework for language-based environment manipulation tasks that can deal with various environments using the same generative language model. |
| Outcome: | The proposed framework achieves new state-of-the-art results on four of the tasks and the execution-guided pre-training strategy brings remarkable improvements on all experimental tasks. |
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 . |
| Outcome: | The proposed dataset avoids a priori pre-defining domain-specific predicates to recognize . instead, it focuses on basic understanding of the expressed semantics rather than reducing them to a simplified state representation. |
Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension (N18-1)
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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. |
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. |
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. |
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. |