Papers with weakly supervised approach
Temporal Event Knowledge Acquisition via Identifying Narratives (P18-1)
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| Challenge: | Existing knowledge of narrative examples is lacking and difficult to obtain. |
| Approach: | They propose a weakly supervised approach for acquiring rich temporal event knowledge across sentences in narrative stories. |
| Outcome: | The proposed approach outperforms neural network models on the narrative cloze task. |
Learning Action Conditions from Instructional Manuals for Instruction Understanding (2023.acl-long)
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| Challenge: | a weakly supervised task is proposed to extract mentions of preconditions and postconditions of actions from instructional manuals. |
| Approach: | They propose a task dubbed action condition inference which extracts mentions of preconditions and postconditions of actions from instructional manuals. |
| Outcome: | The proposed approach improves on the existing models, but still far behind human performance. |
Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking (2022.findings-acl)
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| Challenge: | Existing methods for predicting research replication are insufficient especially for long research papers. |
| Approach: | They propose to build an interpretable neural model which can provide sentence-level explanations and apply weakly supervised approach to leverage large corpus of unlabeled datasets. |
| Outcome: | The proposed model can provide sentence-level explanations and leverage large unlabeled datasets to boost interpretability and improve prediction performance. |
Weakly Supervised Subevent Knowledge Acquisition (2020.emnlp-main)
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| Challenge: | Subevents elaborate an event and exist in event descriptions. |
| Approach: | They propose a weakly supervised approach to extract subevent relation tuples from text . they then use the initial seed subeven pairs to train a contextual classifier . |
| Outcome: | The proposed method is high quality and covers a wide range of event types. |
Learning Concept Abstractness Using Weak Supervision (D18-1)
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Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim
| Challenge: | Existing methods for inferring abstractness of words and expressions without labeled data are limited and limited. |
| Approach: | They propose a weakly supervised approach for inferring the property of abstractness of words and expressions in the absence of labeled data. |
| Outcome: | The proposed approach obtains high correlation with human labels in the absence of labeled data. |