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. |
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| Challenge: | Using a crowdsourced corpus of 25,000 event phrases, we construct a new task that uses commonsense reasoning to reason about the likely intents and reactions of the event participants. |
| Approach: | They construct a crowdsourced corpus of 25,000 event phrases and use them to construct 'commonsense inference' they demonstrate that neural encoder-decoder models can compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. |
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Mapping Texts to Scripts: An Entailment Study (L18-1)
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| Challenge: | Script knowledge is crucial for text understanding systems, providing a basis for commonsense inference. |
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Debiasing Event Understanding for Visual Commonsense Tasks (2022.findings-acl)
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| Challenge: | a recent study shows that object-based event understanding is purely likelihood-based, leading to incorrect event prediction. |
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A Method for Building a Commonsense Inference Dataset based on Basic Events (2020.emnlp-main)
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| Challenge: | Existing approaches to acquire commonsense are limited by the general-purpose language models. |
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Integrating External Event Knowledge for Script Learning (2020.coling-main)
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| Challenge: | Recent studies focus on event co-occurrence to solve this problem. |
| Approach: | They propose to integrate external event knowledge to help predict the next event . they use a script-based approach that integrates external event information into the model . |
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Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning (2021.findings-acl)
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| Challenge: | Existing methods focus on graph triples with event overlap, but ignore more supportive triples . Script reasoning relies on understanding the relationship between two events . |
| Approach: | They propose a model to learn the inferential relations between events from the whole eventuality KG . they propose 'script adapter' to extend the model to infer the associated relations between an event chain and a subsequent event candidate. |
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Learning Event Graph Knowledge for Abductive Reasoning (2021.acl-long)
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| Challenge: | Existing models for abductive reasoning based on formal logic lack commonsense knowledge and effective reasoning mechanism. |
| Approach: | They propose a narrative text-based abductive reasoning task NLI with a latent variable to capture commonsense knowledge from event graph for guiding the abductive reasoning task. |
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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. |
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EventKE: Event-Enhanced Knowledge Graph Embedding (2021.findings-emnlp)
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| Challenge: | Experimental results show that events can greatly improve the quality of KG embeddings on multiple downstream tasks. |
| Approach: | They propose an event-enhanced KG embedding model that incorporates events into KGs . they first incorporate event nodes by building a heterogeneous network with event argument links . |
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Event-Centric Natural Language Processing (2021.acl-tutorials)
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| Challenge: | This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations. |
| Approach: | This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks. |
| Outcome: | This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks. |