Search Results for author: Hiroki Ouchi

Found 28 papers, 11 papers with code

Japanese Lexical Complexity for Non-Native Readers: A New Dataset

2 code implementations30 Jun 2023 Yusuke Ide, Masato Mita, Adam Nohejl, Hiroki Ouchi, Taro Watanabe

Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale.

Lexical Complexity Prediction

Second Language Acquisition of Neural Language Models

no code implementations5 Jun 2023 Miyu Oba, Tatsuki Kuribayashi, Hiroki Ouchi, Taro Watanabe

With the success of neural language models (LMs), their language acquisition has gained much attention.

Cross-Lingual Transfer Language Acquisition

Arukikata Travelogue Dataset

no code implementations19 May 2023 Hiroki Ouchi, Hiroyuki Shindo, Shoko Wakamiya, Yuki Matsuda, Naoya Inoue, Shohei Higashiyama, Satoshi Nakamura, Taro Watanabe

We have constructed Arukikata Travelogue Dataset and released it free of charge for academic research.

N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models

1 code implementation SIGDIAL (ACL) 2022 Shiki Sato, Reina Akama, Hiroki Ouchi, Ryoko Tokuhisa, Jun Suzuki, Kentaro Inui

In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list.

Response Generation

Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution

1 code implementation EMNLP 2021 Ryuto Konno, Shun Kiyono, Yuichiroh Matsubayashi, Hiroki Ouchi, Kentaro Inui

Masked language models (MLMs) have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR).

An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution

no code implementations COLING 2020 Ryuto Konno, Yuichiroh Matsubayashi, Shun Kiyono, Hiroki Ouchi, Ryo Takahashi, Kentaro Inui

This study addresses two underexplored issues on CDA, that is, how to reduce the computational cost of data augmentation and how to ensure the quality of the generated data.

Data Augmentation Language Modelling +4

Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring

no code implementations13 Oct 2020 Farjana Sultana Mim, Naoya Inoue, Paul Reisert, Hiroki Ouchi, Kentaro Inui

Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation.

Automated Essay Scoring Language Modelling +3

Evaluating Dialogue Generation Systems via Response Selection

1 code implementation ACL 2020 Shiki Sato, Reina Akama, Hiroki Ouchi, Jun Suzuki, Kentaro Inui

Existing automatic evaluation metrics for open-domain dialogue response generation systems correlate poorly with human evaluation.

Dialogue Generation Response Generation +1

Inject Rubrics into Short Answer Grading System

no code implementations WS 2019 Tianqi Wang, Naoya Inoue, Hiroki Ouchi, Tomoya Mizumoto, Kentaro Inui

Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance.

Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring

no code implementations WS 2019 Tomoya Mizumoto, Hiroki Ouchi, Yoriko Isobe, Paul Reisert, Ryo Nagata, Satoshi Sekine, Kentaro Inui

This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts.

An Empirical Study of Span Representations in Argumentation Structure Parsing

no code implementations ACL 2019 Tatsuki Kuribayashi, Hiroki Ouchi, Naoya Inoue, Paul Reisert, Toshinori Miyoshi, Jun Suzuki, Kentaro Inui

For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established.

The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4

no code implementations SEMEVAL 2019 Kazuaki Hanawa, Shota Sasaki, Hiroki Ouchi, Jun Suzuki, Kentaro Inui

Our system achieved 80. 9{\%} accuracy on the test set for the formal run and got the 3rd place out of 42 teams.


Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis

no code implementations ACL 2017 Hiroki Ouchi, Hiroyuki Shindo, Yuji Matsumoto

The performance of Japanese predicate argument structure (PAS) analysis has improved in recent years thanks to the joint modeling of interactions between multiple predicates.

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