Gaku Morio


2020

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Hitachi at SemEval-2020 Task 8 : Simple but Effective Modality Ensemble for Meme Emotion RecognitionSemEval-2020 Task 8: Simple but Effective Modality Ensemble for Meme Emotion Recognition
Terufumi Morishita | Gaku Morio | Shota Horiguchi | Hiroaki Ozaki | Toshinori Miyoshi
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Users of social networking services often share their emotions via multi-modal content, usually images paired with text embedded in them. SemEval-2020 task 8, Memotion Analysis, aims at automatically recognizing these emotions of so-called internet memes. In this paper, we propose a simple but effective Modality Ensemble that incorporates visual and textual deep-learning models, which are independently trained, rather than providing a single multi-modal joint network. To this end, we first fine-tune four pre-trained visual models (i.e., Inception-ResNet, PolyNet, SENet, and PNASNet) and four textual models (i.e., BERT, GPT-2, Transformer-XL, and XLNet). Then, we fuse their predictions with ensemble methods to effectively capture cross-modal correlations. The experiments performed on dev-set show that both visual and textual features aided each other, especially in subtask-C, and consequently, our system ranked 2nd on subtask-C.

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Hitachi at MRP 2020 : Text-to-Graph-Notation TransducerMRP 2020: Text-to-Graph-Notation Transducer
Hiroaki Ozaki | Gaku Morio | Yuta Koreeda | Terufumi Morishita | Toshinori Miyoshi
Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing

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. We propose to unify these tasks as a text-to-graph-notation transduction in which we convert an input text into a graph notation. To this end, we designed a novel Plain Graph Notation (PGN) that handles various graphs universally. Then, our parser predicts a PGN-based sequence by leveraging Transformers and biaffine attentions. Notably, our parser can handle any PGN-formatted graphs with fewer framework-specific modifications. As a result, ensemble versions of the parser tied for 1st place in both cross-framework and cross-lingual tracks.

2019

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Revealing and Predicting Online Persuasion Strategy with Elementary Units
Gaku Morio | Ryo Egawa | Katsuhide Fujita
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In online arguments, identifying how users construct their arguments to persuade others is important in order to understand a persuasive strategy directly. However, existing research lacks empirical investigations on highly semantic aspects of elementary units (EUs), such as propositions for a persuasive online argument. Therefore, this paper focuses on a pilot study, revealing a persuasion strategy using EUs. Our contributions are as follows : (1) annotating five types of EUs in a persuasive forum, the so-called ChangeMyView, (2) revealing both intuitive and non-intuitive strategic insights for the persuasion by analyzing 4612 annotated EUs, and (3) proposing baseline neural models that identify the EU boundary and type. Our observations imply that EUs definitively characterize online persuasion strategies.

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Annotating and Analyzing Semantic Role of Elementary Units and Relations in Online Persuasive Arguments
Ryo Egawa | Gaku Morio | Katsuhide Fujita
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

For analyzing online persuasions, one of the important goals is to semantically understand how people construct comments to persuade others. However, analyzing the semantic role of arguments for online persuasion has been less emphasized. Therefore, in this study, we propose a novel annotation scheme that captures the semantic role of arguments in a popular online persuasion forum, so-called ChangeMyView. Through this study, we have made the following contributions : (i) proposing a scheme that includes five types of elementary units (EUs) and two types of relations. (ii) annotating ChangeMyView which results in 4612 EUs and 2713 relations in 345 posts. (iii) analyzing the semantic role of persuasive arguments. Our analyses captured certain characteristic phenomena for online persuasion.

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Hitachi at MRP 2019 : Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation ParsingMRP 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.

2018

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End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture
Gaku Morio | Katsuhide Fujita
Proceedings of the 5th Workshop on Argument Mining

Argument Mining (AM) is a relatively recent discipline, which concentrates on extracting claims or premises from discourses, and inferring their structures. However, many existing works do not consider micro-level AM studies on discussion threads sufficiently. In this paper, we tackle AM for discussion threads. Our main contributions are follows : (1) A novel combination scheme focusing on micro-level inner- and inter- post schemes for a discussion thread. (2) Annotation of large-scale civic discussion threads with the scheme. (3) Parallel constrained pointer architecture (PCPA), a novel end-to-end technique to discriminate sentence types, inner-post relations, and inter-post interactions simultaneously. The experimental results demonstrate that our proposed model shows better accuracy in terms of relations extraction, in comparison to existing state-of-the-art models.Parallel constrained pointer architecture (PCPA), a novel end-to-end technique to discriminate sentence types, inner-post relations, and inter-post interactions simultaneously. The experimental results demonstrate that our proposed model shows better accuracy in terms of relations extraction, in comparison to existing state-of-the-art models.