Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text

Dalvi, Bhavana; Tandon, Niket; Bosselut, Antoine; Yih, Wen-tau; Clark, Peter E.

doi:10.18653/v1/d19-1457

Cited by 16 publications

(8 citation statements)

References 23 publications

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“…This benchmark can serve the research community around this area to evaluate their new techniques against a set of tasks and configurations to analyze multiple aspects of new techniques. In the future, we plan to extend the tasks of this benchmark to include more applications, such as spatial reasoning over natural language (Mirzaee et al 2021b), visual question answering (Huang et al 2021), procedural reasoning Dalvi et al 2019), and event-event relationship extraction (Wang et al 2020).…”

Section: Discussionmentioning

confidence: 99%

GLUECons: A Generic Benchmark for Learning under Constraints

Faghihi

Nafar

Chen

et al. 2023

AAAI

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Recent research has shown that integrating domain knowledge into deep learning architectures is effective; It helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models. However, the research community lacks a convened benchmark for systematically evaluating knowledge integration methods. In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints. We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying related research challenges. It will facilitate further research for alleviating some problems of state-of-the-art neural models.

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Section: Discussionmentioning

confidence: 99%

GLUECons: A Generic Benchmark for Learning under Constraints

Faghihi

Nafar

Chen

et al. 2023

AAAI

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“…Yang et al, 2018;Khot et al, 2020;Xie et al, 2020;Dalvi et al, 2021) or procedural knowledge prediction (e.g. Dalvi et al, 2019). A contemporary challenge is that the number of valid compositional procedures is typically large compared to those that can be tractably annotated as gold, and as such automatically evaluating model performance becomes challenging .…”

Section: Why Use Text Worlds?mentioning

confidence: 99%

A Systematic Survey of Text Worlds as Embodied Natural Language Environments

Jansen¹

2022

Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022)

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Text Worlds are virtual environments for embodied agents that, unlike 2D or 3D environments, are rendered exclusively using textual descriptions. These environments offer an alternative to higher-fidelity 3D environments due to their low barrier to entry, providing the ability to study semantics, compositional inference, and other high-level tasks with rich action spaces while controlling for perceptual input. This systematic survey outlines recent developments in tooling, environments, and agent modeling for Text Worlds, while examining recent trends in knowledge graphs, common sense reasoning, transfer learning of Text World performance to higher-fidelity environments, as well as near-term development targets that, once achieved, make Text Worlds an attractive general research paradigm for natural language processing.

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“…The goal is to track the sequence of state changes (e.g., creation and movement) entities undergo over long sequences of procedure steps. Dalvi et al (2019) propose to use the WikiHow data to train the state change tracking model with limited states, which is an open-domain procedural text dataset. Past work involves both modeling entities across procedure steps (Das et al, 2019;Tang et al, 2020;Kiddon et al, 2015;Gupta and Durrett, 2019b).…”

Section: State Tracking In Procedural Textmentioning

confidence: 99%

“…WikiHow proc is a modified version based on WikiHow dataset (Koupaee and Wang, 2018) which contains articles describing procedural tasks about various topics (from arts and entertainment to computers and electronics) with multiple steps. Many existing procedural text state analysis methods (Tandon et al, 2020;Zhang et al, 2020c,a;Dalvi et al, 2019;Goyal et al, 2021) have conducted experiments on the WikiHow dataset, and this is the benchmark dataset on procedural text modeling. Each article consists of multiple paragraphs and each paragraph starts with a sentence summarizing it.…”

Section: Datasetmentioning

confidence: 99%

Summarizing Procedural Text: Data and Approach

Gao¹,

Zhang²,

Chen³

et al. 2022

Findings of the Association for Computational Linguistics: EMNLP 2022

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Procedural text is a widely used genre that contains many steps of instructions of how to cook a dish or how to conduct a chemical experiment and analyzing the procedural text has become a popular task in the NLP field. Since the procedural text can be very long and contains many details, summarizing the whole procedural text or giving an overview for each complicated procedure step can save time for readers and help the reader to capture the core action in the procedure. In this paper, we propose the procedural text summarization task with two summarization granularity: step-view and globalview, which summarizes each step in procedural text separately or gives an overall summary for all steps respectively. To tackle this task, we propose an Entity-State Graph-based Summarizer (ESGS) which is based on stateof-the-art entity state tracking methods and constructs a heterogeneous graph to aggregate contextual information for each procedure. In order to help the summarization model focus on the salient entity, we propose to use the contextualized procedure graph representation to predict the salient entity. Experiments conducted on two datasets verify the effectiveness of our proposed model, and the code and datasets will be released on https://github. com/gsh199449/procedural-summ.Li Zhang, Qing Lyu, and Chris Callison-Burch. 2020c.Reasoning about goals, steps, and temporal ordering with wikihow. In EMNLP.Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. In ACL.

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Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text

Cited by 16 publications

References 23 publications

GLUECons: A Generic Benchmark for Learning under Constraints

GLUECons: A Generic Benchmark for Learning under Constraints

A Systematic Survey of Text Worlds as Embodied Natural Language Environments

Summarizing Procedural Text: Data and Approach

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