Search Results for author: Gaku Morio

Found 17 papers, 0 papers with code

Rethinking Fano's Inequality in Ensemble Learning

no code implementations25 May 2022 Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki, Nobuo Nukaga

We propose a fundamental theory on ensemble learning that evaluates a given ensemble system by a well-grounded set of metrics.

Ensemble Learning

Hitachi at SemEval-2020 Task 7: Stacking at Scale with Heterogeneous Language Models for Humor Recognition

no code implementations SEMEVAL 2020 Terufumi Morishita, Gaku Morio, Hiroaki Ozaki, Toshinori Miyoshi

Our experimental results show that SaS outperforms a naive average ensemble, leveraging weaker PLMs as well as high-performing PLMs.

Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity

no code implementations SEMEVAL 2020 Terufumi Morishita, Gaku Morio, Hiroaki Ozaki, Toshinori Miyoshi

Due to the unsupervised nature of the task, we concentrated on inquiring about the similarity measures induced by different layers of different pre-trained Transformer-based language models, which can be good approximations of the human sense of word similarity.

Word Similarity

Hitachi at MRP 2020: Text-to-Graph-Notation Transducer

no code implementations CONLL 2020 Hiroaki Ozaki, Gaku Morio, Yuta Koreeda, Terufumi Morishita, Toshinori Miyoshi

This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages.

Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization

no code implementations ACL 2020 Gaku Morio, Hiroaki Ozaki, Terufumi Morishita, Yuta Koreeda, Kohsuke Yanai

Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges.

Argument Mining

Corpus for Modeling User Interactions in Online Persuasive Discussions

no code implementations LREC 2020 Ryo Egawa, Gaku Morio, Katsuhide Fujita

To analyze persuasive strategies, it is important to understand how individuals construct posts and comments based on the semantics of the argumentative components.

Argument Mining

Revealing and Predicting Online Persuasion Strategy with Elementary Units

no code implementations IJCNLP 2019 Gaku Morio, Ryo Egawa, Katsuhide Fujita

In online arguments, identifying how users construct their arguments to persuade others is important in order to understand a persuasive strategy directly.

Persuasion Strategies

Annotating and Analyzing Semantic Role of Elementary Units and Relations in Online Persuasive Arguments

no code implementations ACL 2019 Ryo Egawa, Gaku Morio, Katsuhide Fujita

For analyzing online persuasions, one of the important goals is to semantically understand how people construct comments to persuade others.

End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture

no code implementations WS 2018 Gaku Morio, Katsuhide Fujita

Argument Mining (AM) is a relatively recent discipline, which concentrates on extracting claims or premises from discourses, and inferring their structures.

Argument Mining

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