Hirotaka Kameko


Image Description Dataset for Language Learners
Kento Tanaka | Taichi Nishimura | Hiroaki Nanjo | Keisuke Shirai | Hirotaka Kameko | Masatake Dantsuji
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We focus on image description and a corresponding assessment system for language learners. To achieve automatic assessment of image description, we construct a novel dataset, the Language Learner Image Description (LLID) dataset, which consists of images, their descriptions, and assessment annotations. Then, we propose a novel task of automatic error correction for image description, and we develop a baseline model that encodes multimodal information from a learner sentence with an image and accurately decodes a corrected sentence. Our experimental results show that the developed model can revise errors that cannot be revised without an image.

Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows
Keisuke Shirai | Atsushi Hashimoto | Taichi Nishimura | Hirotaka Kameko | Shuhei Kurita | Yoshitaka Ushiku | Shinsuke Mori
Proceedings of the 29th International Conference on Computational Linguistics

We present a new multimodal dataset called Visual Recipe Flow, which enables us to learn a cooking action result for each object in a recipe text. The dataset consists of object state changes and the workflow of the recipe text. The state change is represented as an image pair, while the workflow is represented as a recipe flow graph. We developed a web interface to reduce human annotation costs. The dataset allows us to try various applications, including multimodal information retrieval.


Annotating Event Appearance for Japanese Chess Commentary Corpus
Hirotaka Kameko | Shinsuke Mori
Proceedings of the Twelfth Language Resources and Evaluation Conference

In recent years, there has been a surge of interest in natural language processing related to the real world, such as symbol grounding, language generation, and non-linguistic data search by natural language queries. Researchers usually collect pairs of text and non-text data for research. However, the text and non-text data are not always a “true” pair. We focused on the shogi (Japanese chess) commentaries, which are accompanied by game states as a well-defined “real world”. For analyzing and processing texts accurately, considering only the given states is insufficient, and we must consider the relationship between texts and the real world. In this paper, we propose “Event Appearance” labels that show the relationship between events mentioned in texts and those happening in the real world. Our event appearance label set consists of temporal relation, appearance probability, and evidence of the event. Statistics of the annotated corpus and the experimental result show that there exists temporal relation which skillful annotators realize in common. However, it is hard to predict the relationship only by considering the given states.


Annotating Modality Expressions and Event Factuality for a Japanese Chess Commentary Corpus
Suguru Matsuyoshi | Hirotaka Kameko | Yugo Murawaki | Shinsuke Mori
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


A Japanese Chess Commentary Corpus
Shinsuke Mori | John Richardson | Atsushi Ushiku | Tetsuro Sasada | Hirotaka Kameko | Yoshimasa Tsuruoka
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In recent years there has been a surge of interest in the natural language prosessing related to the real world, such as symbol grounding, language generation, and nonlinguistic data search by natural language queries. In order to concentrate on language ambiguities, we propose to use a well-defined “real world,” that is game states. We built a corpus consisting of pairs of sentences and a game state. The game we focus on is shogi (Japanese chess). We collected 742,286 commentary sentences in Japanese. They are spontaneously generated contrary to natural language annotations in many image datasets provided by human workers on Amazon Mechanical Turk. We defined domain specific named entities and we segmented 2,508 sentences into words manually and annotated each word with a named entity tag. We describe a detailed definition of named entities and show some statistics of our game commentary corpus. We also show the results of the experiments of word segmentation and named entity recognition. The accuracies are as high as those on general domain texts indicating that we are ready to tackle various new problems related to the real world.


Can Symbol Grounding Improve Low-Level NLP? Word Segmentation as a Case Study
Hirotaka Kameko | Shinsuke Mori | Yoshimasa Tsuruoka
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing