Papers by Hiroaki Ozaki

4 papers
How does the task complexity of masked pretraining objectives affect downstream performance? (2023.findings-acl)

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Challenge: Masked language modeling (MLM) is a widely used self-supervised pretraining objective.
Approach: They propose to use a mask-based objective to predict a token that is replaced with a masked token given its context.
Outcome: The proposed objectives show that they should have half the complexity needed to perform comparably to MLM.
Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization (2020.acl-main)

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Challenge: Argument mining studies have advanced the ability to predict argument structures, but the technology for capturing non-tree-structured arguments is still in its infancy.
Approach: They propose a neural model that can predict proposition types and edges between propositions.
Outcome: The proposed model improves edge prediction performance compared to baseline models.
End-to-end Argument Mining with Cross-corpora Multi-task Learning (2022.tacl-1)

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Challenge: Argument(ation) mining is a task of identifying argument structure from text . lack of training data makes it difficult to train models based on limited data sets.
Approach: They propose an end-to-end cross-corpus argument mining method that uses auxiliary argument mining corpora to train models.
Outcome: The proposed method outperforms models trained on a single corpus on arguments on arguments in argument mining tasks.
Project-then-Transfer: Effective Two-stage Cross-lingual Transfer for Semantic Dependency Parsing (2021.eacl-main)

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Challenge: Several remarkable contributions have been made in syntactic dependency parsing, especially on universal dependencies.
Approach: They propose to capture cross-linguality by combing annotation projection and model transfer of pre-trained language models.
Outcome: The proposed model parser almost achieved the approximated upper bound.

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