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. |
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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 about Actions and State Changes by Injecting Commonsense Knowledge (D18-1)
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| Challenge: | Recent work has shown impressive progress in comprehending procedural text, but their predictions can be inconsistent or highly improbable. |
| Approach: | They propose to incorporate global constraints and bias reading with corpora-based preferences to improve the predicted effects of actions in a paragraph. |
| Outcome: | The proposed model significantly outperforms earlier models on a benchmark dataset for procedural text comprehension (+8% relative gain) it avoids nonsensical predictions that earlier models make, and it is more robust than previous models. |
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. |
Harry Potter and the Action Prediction Challenge from Natural Language (N19-1)
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| Challenge: | Using textual descriptions of scenes, we explore the challenge of action prediction from textual description. |
| Approach: | They propose a testbed to approximate whether text inference can be used to predict upcoming actions from textual descriptions of scenes. |
| Outcome: | The proposed model performs best for frequent actions and large scene descriptions, but logistic regression fails on infrequent actions. |
What-if I ask you to explain: Explaining the effects of perturbations in procedural text (2020.findings-emnlp)
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| Challenge: | QUARTET constructs explanations from paragraphs using procedural text . qartet achieves 18 points better on explanation accuracy compared to strong baselines on a recent process comprehension benchmark. |
| Approach: | They propose a system that constructs explanations from paragraphs by modeling the explanation task as a multitask learning problem. |
| Outcome: | The proposed system achieves 18 points better on explanation accuracy compared to strong baselines on a process comprehension benchmark. |
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. |
What Action Causes This? Towards Naive Physical Action-Effect Prediction (P18-1)
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| Challenge: | a new task on naive physical action-effect prediction addresses the relationship between concrete actions and their effects on the state of the physical world as depicted by images. |
| Approach: | They propose a task that harnesses web image data to facilitate action-effect prediction. |
| Outcome: | The proposed approach harnesses web image data through distant supervision to facilitate learning for action-effect prediction. |
Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis (2021.naacl-main)
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| Challenge: | Existing studies focus on analyzing structured data, while mining causal relationship among factors from unstructured data is of great importance. |
| Approach: | They propose a graph-based causal inference framework which builds causal graphs from fact descriptions without much human involvement. |
| Outcome: | The proposed framework can capture nuance from fact descriptions among confusing charges and provide explainable discrimination in few-shot settings. |
Relevant CommonSense Subgraphs for “What if...” Procedural Reasoning (2022.findings-acl)
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| Challenge: | Existing knowledge graphs and commonsense are used to learn causal reasoning over procedural text. |
| Approach: | They propose a multi-hop graph reasoning model to efficiently extract a commonsense subgraph with the most relevant information from a large knowledge graph and predict the causal answer by reasoning over the representations obtained from the commonsen subgraph and contextual interactions between the questions and context. |
| Outcome: | The proposed model achieves state-of-the-art on WIQA benchmark and is comparable to previous models. |