Papers with local classifiers
ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Existing methods to find relational facts from texts lack hierarchical information of relations. |
| Approach: | They propose a hierarchical classification framework which extracts relation in a top-down manner. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on NYT dataset . the proposed method generates large amounts of training data by aligning KBs with unlabeled corpora . |
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks (2020.emnlp-main)
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| Challenge: | Recent work shows that conditional random fields (CRFs) perform well in sequence labeling tasks. |
| Approach: | They propose several high-order energy terms to capture dependencies among labels in sequence labeling . they use convolutional, recurrent, and self-attention networks to construct these energy terms . |
| Outcome: | The proposed approach improves on four sequence labeling tasks while having the same decoding speed as simple classifiers. |
A Joint Neural Model for Information Extraction with Global Features (2020.acl-main)
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| Challenge: | Existing joint neural models for Information Extraction use local task-specific classifiers to predict labels for individual instances. |
| Approach: | They propose a joint neural framework that extracts the optimal IE result as a graph from an input sentence. |
| Outcome: | The proposed model achieves new state-of-the-art on all subtasks and does not use any language-specific feature. |
Information Extraction with Differentiable Beam Search on Graph RNNs (2024.lrec-main)
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| Challenge: | Existing approaches to information extraction suffer from exposure bias due to discrepancy between training and decoding. |
| Approach: | They propose to cast graph generation as auto-regressive sequence labeling and make it aware of decoding procedure by using differentiable beam search. |
| Outcome: | The proposed model outperforms its non-decoding-aware version on ACE05 and ConLL04 datasets. |