Papers with integrated models
Improving Relation Extraction with Knowledge-attention (D19-1)
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| Challenge: | Existing attention mechanisms are data-driven, but most are data driven. |
| Approach: | They propose a knowledge-attention encoder which integrates prior knowledge from external lexical resources into deep neural networks for relation extraction task. |
| Outcome: | The proposed system outperforms existing CNN, RNN, and self-attention based models on a large-scale relation extraction dataset. |
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)
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| Challenge: | Mainstream methods that ignore the diversity among keyphrases or weakly capture the relation between tasks implicitly ignore keyphrase diversity. |
| Approach: | They propose a novel end-to-end learning framework that jointly learns to extract and generate keyphrases by exploiting latent semantic relation between extraction and generation. |
| Outcome: | The proposed approach outperforms mainstream methods on a benchmarked document on keyphrase prediction. |
The Role of Semantic Parsing in Understanding Procedural Text (2023.findings-eacl)
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| Challenge: | Inferring actions and their impact on entities involved in a procedural text can be challenging in various aspects. |
| Approach: | They propose a symbolic parser and semantic role labeling as two sources of semantic parsing knowledge. |
| Outcome: | The proposed framework integrates semantic parsing knowledge into state-of-the-art neural models and shows that it improves procedural understanding. |