Kohsuke Yanai


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Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization
Gaku Morio | Hiroaki Ozaki | Terufumi Morishita | Yuta Koreeda | Kohsuke Yanai
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

State-of-the-art argument mining studies have advanced the techniques for predicting argument structures. However, the technology for capturing non-tree-structured arguments is still in its infancy. In this paper, we focus on non-tree argument mining with a neural model. We jointly predict proposition types and edges between propositions. 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. Experimental results show that both TSP and PLBA boost edge prediction performance compared to baselines.


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Hitachi at MRP 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing
Yuta Koreeda | Gaku Morio | Terufumi Morishita | Hiroaki Ozaki | Kohsuke Yanai
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

This paper describes the proposed system of the Hitachi team for the Cross-Framework Meaning Representation Parsing (MRP 2019) shared task. In this shared task, the participating systems were asked to predict nodes, edges and their attributes for five frameworks, each with different order of “abstraction” from input tokens. We proposed a unified encoder-to-biaffine network for all five frameworks, which effectively incorporates a shared encoder to extract rich input features, decoder networks to generate anchorless nodes in UCCA and AMR, and biaffine networks to predict edges. Our system was ranked fifth with the macro-averaged MRP F1 score of 0.7604, and outperformed the baseline unified transition-based MRP. Furthermore, post-evaluation experiments showed that we can boost the performance of the proposed system by incorporating multi-task learning, whereas the baseline could not. These imply efficacy of incorporating the biaffine network to the shared architecture for MRP and that learning heterogeneous meaning representations at once can boost the system performance.


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bunji at SemEval-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility Features
Yuta Koreeda | Takuya Hashito | Yoshiki Niwa | Misa Sato | Toshihiko Yanase | Kenzo Kurotsuchi | Kohsuke Yanai
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes a text-ranking system developed by bunji team in SemEval-2017 Task 3: Community Question Answering, Subtask A and C. The goal of the task is to re-rank the comments in a question-and-answer forum such that useful comments for answering the question are ranked high. We proposed a method that combines neural similarity features and hand-crafted comment plausibility features, and we modeled inter-comments relationship using conditional random field. Our approach obtained the fifth place in the Subtask A and the second place in the Subtask C.

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StruAP: A Tool for Bundling Linguistic Trees through Structure-based Abstract Pattern
Kohsuke Yanai | Misa Sato | Toshihiko Yanase | Kenzo Kurotsuchi | Yuta Koreeda | Yoshiki Niwa
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present a tool for developing tree structure patterns that makes it easy to define the relations among textual phrases and create a search index for these newly defined relations. By using the proposed tool, users develop tree structure patterns through abstracting syntax trees. The tool features (1) intuitive pattern syntax, (2) unique functions such as recursive call of patterns and use of lexicon dictionaries, and (3) whole workflow support for relation development and validation. We report the current implementation of the tool and its effectiveness.


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Neural Attention Model for Classification of Sentences that Support Promoting/Suppressing Relationship
Yuta Koreeda | Toshihiko Yanase | Kohsuke Yanai | Misa Sato | Yoshiki Niwa
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

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bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis
Toshihiko Yanase | Kohsuke Yanai | Misa Sato | Toshinori Miyoshi | Yoshiki Niwa
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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Learning Sentence Ordering for Opinion Generation of Debate
Toshihiko Yanase | Toshinori Miyoshi | Kohsuke Yanai | Misa Sato | Makoto Iwayama | Yoshiki Niwa | Paul Reisert | Kentaro Inui
Proceedings of the 2nd Workshop on Argumentation Mining

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End-to-end Argument Generation System in Debating
Misa Sato | Kohsuke Yanai | Toshinori Miyoshi | Toshihiko Yanase | Makoto Iwayama | Qinghua Sun | Yoshiki Niwa
Proceedings of ACL-IJCNLP 2015 System Demonstrations