| 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. |
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DocScript: Document-level Script Event Prediction (2024.lrec-main)
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Puneet Mathur, Vlad I. Morariu, Aparna Garimella, Franck Dernoncourt, Jiuxiang Gu, Ramit Sawhney, Preslav Nakov, Dinesh Manocha, Rajiv Jain
| Challenge: | Existing script event prediction frameworks such as ChatGPT and FlanT5 lack the ability to learn long-range dependencies between events. |
| Approach: | They propose a novel script event prediction task which aims to predict the next event from a candidate list of narrative events in long-form documents. |
| Outcome: | The proposed architecture can learn sequential ordering between events at the document scale. |
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. |
Neural Language Modeling for Contextualized Temporal Graph Generation (2021.naacl-main)
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| Challenge: | Existing methods for temporal reasoning have been used for a number of applications, but their potential for tempor reasoning over event graphs has not been explored. |
| Approach: | They propose to use large-scale pre-trained language models to generate an event-level temporal graph from a document using existing IE/NLP tools. |
| Outcome: | The proposed method outperforms the closest existing method on several metrics on a hand-labeled, out-of-domain corpus. |
NGEP: A Graph-based Event Planning Framework for Story Generation (2022.aacl-short)
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| Challenge: | Current approaches to story generation are based on end-to-end neural generation models, such as BART, to generate event sequences. |
| Approach: | They propose a novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) event planning approaches on multiple criteria and compares with existing models on the downstream task of story generation. |
Benchmarking Hierarchical Script Knowledge (N19-1)
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| 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. |
Exploring Pre-trained Language Models for Event Extraction and Generation (P19-1)
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| Challenge: | Existing methods to extract event data are laborious to create and limited in size. |
| Approach: | They propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. |
| Outcome: | The proposed method surpasses existing methods on the ACE2005 dataset and improves on the previous methods. |
Don’t Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models (2021.acl-short)
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| Challenge: | Existing approaches to train event language models on text constrain them to follow discourse order of events. |
| Approach: | They propose a method to perturb event sequences so that they can relax model dependence on text order. |
| Outcome: | The proposed technique improves performance on applications and out-of-domain events data. |
Event Representation Learning Enhanced with External Commonsense Knowledge (D19-1)
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| Challenge: | Existing methods to learn event representations from text lack commonsense knowledge about the intents and emotions of event participants. |
| Approach: | They propose to leverage external commonsense knowledge about the intent and sentiment of the event to learn distributed representations for structured events from text. |
| Outcome: | The proposed model improves on hard similarity tasks and yields more precise inferences on subsequent events under given contexts. |
Language Models of Code are Few-Shot Commonsense Learners (2022.emnlp-main)
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| Challenge: | Existing approaches to generate graphs using pre-trained language models hinder their ability to generate them correctly. |
| Approach: | They propose to frame structured commonsense reasoning tasks as code generation tasks instead of serializing the output graph as a flat list of nodes and edges. |
| Outcome: | The proposed approach outperforms natural-language LMs in three natural language tasks even when the downstream task does not involve source code at all. |
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning (2022.acl-long)
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| Challenge: | Pre-trained sequence-to-sequence language models generate structured outputs such as graphs with limited supervision. |
| Approach: | They propose to use pre-trained sequence-to-sequence language models to generate graphs . they propose to learn structural constraints and semantics of graphs with limited supervision . |
| Outcome: | The proposed models can learn structural constraints and semantics of graphs with limited supervision. |