| Challenge: | Understanding procedural language requires reasoning about hierarchical and temporal relations between events. |
| Approach: | They propose a hierarchical script learning dataset and a cloze task to match video captions with missing procedural details. |
| Outcome: | The proposed model matches video captions with missing procedural details to find out if they can understand the language. |
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Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation (2023.findings-acl)
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| Challenge: | Existing work assumes that events are sequentially arranged in a script, while this assumption leads to linear generation that is far from sufficient for comprehensively acquiring the representation about how events are organized towards a task goal. |
| Approach: | They propose to extend goal-oriented Script Generation task from the perspective of cognitive theory by incorporating subgoals into hierarchical script generation. |
| Outcome: | The proposed task is based on a new dataset and human evaluation metrics. |
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data (2022.acl-long)
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| Challenge: | Existing work has treated procedures as shallow structures without modeling the parent-child relation. |
| Approach: | They propose to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow . they link steps in an article to other articles with similar goals, recursively building the KB . |
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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 . |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
proScript: Partially Ordered Scripts Generation (2021.findings-emnlp)
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| Challenge: | Scripts represent structured commonsense knowledge about prototypical events in everyday situations/scenarios such as bake a cake. |
| Approach: | They collect 6.4k crowdsourced partially ordered scripts and develop models that combine language generation and graph structure prediction to generate scripts. |
| Outcome: | The proposed models perform well on two tasks: edge prediction and script generation. |
Non-Sequential Graph Script Induction via Multimedia Grounding (2023.acl-long)
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| Challenge: | Existing scripts for everyday tasks are presented in a linear manner, which does not reflect the flexibility displayed by people executing tasks in real life. |
| Approach: | They propose to use loosely aligned videos to train a non-sequential graph script induction task by using a multimodal framework to ground procedural videos to WikiHow textual steps. |
| Outcome: | The proposed model outperforms the WikiHow linear baseline by 48.76% . it can predict future steps given a partial step sequence and generate explicit graph scripts . |
CogCompNLP: Your Swiss Army Knife for NLP (L18-1)
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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
| Challenge: | a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks. |
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Predicting Implicit Arguments in Procedural Video Instructions (2025.acl-long)
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| Challenge: | Prior SRL benchmarks often miss implicit arguments, leading to incomplete understanding. |
| Approach: | They propose a dataset that necessitates inferring implicit and explicit arguments from contextual information in multimodal cooking procedures. |
| Outcome: | The proposed dataset achieves a 17% relative improvement in F1-score for what-implicit and a 14.7% improvement for where/with-implicative semantic roles over GPT-4o. |
LLM-driven Instruction Following: Progresses and Concerns (2023.emnlp-tutorial)
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| Challenge: | a tutorial on task instruction is aimed at researchers and practitioners interested in NLP generalization . labeled examples are unlikely to be available in large numbers or do not exist . |
| Approach: | This tutorial will examine the progress of natural language processing (NLP) using labeled examples. authors propose that task instructions act as a novel resource for supervision. |
| Outcome: | This tutorial aims to answer questions about instruction-driven NLP . it focuses on the use of task instructions in a low-shot scenario . |
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge (L18-1)
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| Challenge: | Various approaches for script knowledge extraction and processing have been proposed in recent years. |
| Approach: | They propose a dataset to evaluate natural language understanding approaches based on commonsense knowledge. |
| Outcome: | The proposed dataset provides test cases for the broader natural language understanding community. |