Nobuyuki Shimizu


2020

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A Visually-grounded First-person Dialogue Dataset with Verbal and Non-verbal Responses
Hisashi Kamezawa | Noriki Nishida | Nobuyuki Shimizu | Takashi Miyazaki | Hideki Nakayama
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In real-world dialogue, first-person visual information about where the other speakers are and what they are paying attention to is crucial to understand their intentions. Non-verbal responses also play an important role in social interactions. In this paper, we propose a visually-grounded first-person dialogue (VFD) dataset with verbal and non-verbal responses. The VFD dataset provides manually annotated (1) first-person images of agents, (2) utterances of human speakers, (3) eye-gaze locations of the speakers, and (4) the agents’ verbal and non-verbal responses. We present experimental results obtained using the proposed VFD dataset and recent neural network models (e.g., BERT, ResNet). The results demonstrate that first-person vision helps neural network models correctly understand human intentions, and the production of non-verbal responses is a challenging task like that of verbal responses. Our dataset is publicly available.

2018

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Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps
Nobuyuki Shimizu | Na Rong | Takashi Miyazaki
Proceedings of the 27th International Conference on Computational Linguistics

Visual question answering (VQA) is a challenging task that requires a computer system to understand both a question and an image. While there is much research on VQA in English, there is a lack of datasets for other languages, and English annotation is not directly applicable in those languages. To deal with this, we have created a Japanese VQA dataset by using crowdsourced annotation with images from the Visual Genome dataset. This is the first such dataset in Japanese. As another contribution, we propose a cross-lingual method for making use of English annotation to improve a Japanese VQA system. The proposed method is based on a popular VQA method that uses an attention mechanism. We use attention maps generated from English questions to help improve the Japanese VQA task. The proposed method experimentally performed better than simply using a monolingual corpus, which demonstrates the effectiveness of using attention maps to transfer cross-lingual information.

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Pretraining Sentiment Classifiers with Unlabeled Dialog Data
Toru Shimizu | Nobuyuki Shimizu | Hayato Kobayashi
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

The huge cost of creating labeled training data is a common problem for supervised learning tasks such as sentiment classification. Recent studies showed that pretraining with unlabeled data via a language model can improve the performance of classification models. In this paper, we take the concept a step further by using a conditional language model, instead of a language model. Specifically, we address a sentiment classification task for a tweet analysis service as a case study and propose a pretraining strategy with unlabeled dialog data (tweet-reply pairs) via an encoder-decoder model. Experimental results show that our strategy can improve the performance of sentiment classifiers and outperform several state-of-the-art strategies including language model pretraining.

2016

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Cross-Lingual Image Caption Generation
Takashi Miyazaki | Nobuyuki Shimizu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2010

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Features for Detecting Hedge Cues
Nobuyuki Shimizu | Hiroshi Nakagawa
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task

2009

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Deterministic Shift-Reduce Parsing for Unification-Based Grammars by Using Default Unification
Takashi Ninomiya | Takuya Matsuzaki | Nobuyuki Shimizu | Hiroshi Nakagawa
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

2008

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Modeling Chinese Documents with Topical Word-Character Models
Wei Hu | Nobuyuki Shimizu | Hiroshi Nakagawa | Huanye Sheng
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Metric Learning for Synonym Acquisition
Nobuyuki Shimizu | Masato Hagiwara | Yasuhiro Ogawa | Katsuhiko Toyama | Hiroshi Nakagawa
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Structural Correspondence Learning for Dependency Parsing
Nobuyuki Shimizu | Hiroshi Nakagawa
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Exact Decoding for Jointly Labeling and Chunking Sequences
Nobuyuki Shimizu | Andrew Haas
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Semantic Discourse Segmentation and Labeling for Route Instructions
Nobuyuki Shimizu
Proceedings of the COLING/ACL 2006 Student Research Workshop

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Maximum Spanning Tree Algorithm for Non-projective Labeled Dependency Parsing
Nobuyuki Shimizu
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)

2004

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HITIQA: Towards Analytical Question Answering
Sharon Small | Tomek Strzalkowski | Ting Liu | Sean Ryan | Robert Salkin | Nobuyuki Shimizu | Paul Kantor | Diane Kelly | Robert Rittman | Nina Wacholder
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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HITIQA: Scenario Based Question Answering
Sharon Small | Tomek Strzalkowski | Ting Liu | Sean Ryan | Robert Salkin | Nobuyuki Shimizu | Paul Kantor | Diane Kelly | Robert Rittman | Nina Wacholder | Boris Yamrom
Proceedings of the Workshop on Pragmatics of Question Answering at HLT-NAACL 2004

2003

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HITIQA: An Interactive Question Answering System: A Preliminary Report
Sharon Small | Ting Liu | Nobuyuki Shimizu | Tomek Strzalkowski
Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering