Papers with sequence labeling model

4 papers
Active Learning for New Domains in Natural Language Understanding (N19-2)

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Challenge: Existing approaches to improve the accuracy of new domains are lacking annotated live utterances.
Approach: They propose an algorithm called Majority-CRF that uses an ensemble of classification models to guide the selection of relevant utterances and a sequence labeling model to prioritize informative examples.
Outcome: The proposed algorithm achieves 6.6%-9% error rate reduction and statistically significant improvements on six new domains.
Towards Identifying Alternative-Lexicalization Signals of Discourse Relations (2022.coling-1)

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Challenge: Existing shallow discourse parsing methods have been limited to identifying relations signaled by a discourse connective and those without a signal.
Approach: They propose to identify relations signalled by a discourse connective and those without . they compare a pattern-based approach and a sequence labeling model .
Outcome: The proposed approach is based on a pattern-based approach and a sequence labeling model.
iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples (2024.naacl-long)

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Challenge: Existing methods for extracting aspects and opinions from text are incomplete.
Approach: They propose a method for extracting Implicit Aspects with Categories and Opinions with Sentiments using implicit tokens.
Outcome: The proposed method outperforms baseline methods on two public benchmark datasets.
Grammatical Error Correction as GAN-like Sequence Labeling (2021.findings-acl)

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Challenge: Traditional GEC models learn from sentences with fixed error rates . sequence labeling approaches suffer from a couple of key problems .
Approach: They propose a GAN-like sequence labeling model with a grammatical error detector and a generator to correct grammamatical errors.
Outcome: The proposed model improves the state-of-the-art in GEC and improves on benchmarks.

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