Papers with incremental learning methods

2 papers
Incremental Event Detection via Knowledge Consolidation Networks (2020.emnlp-main)

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Challenge: Existing approaches to event detection require a fixed set of pre-defined event types . existing methods cannot handle semantic ambiguity and training data imbalance problems .
Approach: They propose a Knowledge Consolidation Network to address these issues . they propose to use a prototype enhanced retrospection and hierarchical distillation to mitigate the adverse effects of semantic ambiguity and class imbalance.
Outcome: The proposed method outperforms the state-of-the-art model by 19% and 13.4% of whole F1 score on ACE and TAC benchmarks.
Sentence Embedding Alignment for Lifelong Relation Extraction (N19-1)

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Challenge: Existing approaches to relation extraction require a fixed set of relations . Existing methods assume a closed set of relationships and perform once-and-for-all training on a set of datasets.
Approach: They propose to improve the stochastic gradient methods with a replay memory to alleviate the forgetting problem by anchoring the sentence embedding space.
Outcome: The proposed method outperforms state-of-the-art methods on multiple benchmarks.

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