Yuta Nakashima


2022

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Emotional Intensity Estimation based on Writer’s Personality
Haruya Suzuki | Sora Tarumoto | Tomoyuki Kajiwara | Takashi Ninomiya | Yuta Nakashima | Hajime Nagahara
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Student Research Workshop

We propose a method for personalized emotional intensity estimation based on a writer’s personality test for Japanese SNS posts. Existing emotion analysis models are difficult to accurately estimate the writer’s subjective emotions behind the text. We personalize the emotion analysis using not only the text but also the writer’s personality information. Experimental results show that personality information improves the performance of emotional intensity estimation. Furthermore, a hybrid model combining the existing personalized method with ours achieved state-of-the-art performance.

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A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain
Haruya Suzuki | Yuto Miyauchi | Kazuki Akiyama | Tomoyuki Kajiwara | Takashi Ninomiya | Noriko Takemura | Yuta Nakashima | Hajime Nagahara
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We annotate 35,000 SNS posts with both the writer’s subjective sentiment polarity labels and the reader’s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (none, weak, medium, and strong) for each of the eight basic emotions by Plutchik (joy, sadness, anticipation, surprise, anger, fear, disgust, and trust) as well as sentiment polarity labels (strong positive, positive, neutral, negative, and strong negative). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer’s and reader’s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.

2021

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WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations
Tomoyuki Kajiwara | Chenhui Chu | Noriko Takemura | Yuta Nakashima | Hajime Nagahara
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We annotate 17,000 SNS posts with both the writer’s subjective emotional intensity and the reader’s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer’s subjective labels than the readers’. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.

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Attending Self-Attention: A Case Study of Visually Grounded Supervision in Vision-and-Language Transformers
Jules Samaran | Noa Garcia | Mayu Otani | Chenhui Chu | Yuta Nakashima
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop

The impressive performances of pre-trained visually grounded language models have motivated a growing body of research investigating what has been learned during the pre-training. As a lot of these models are based on Transformers, several studies on the attention mechanisms used by the models to learn to associate phrases with their visual grounding in the image have been conducted. In this work, we investigate how supervising attention directly to learn visual grounding can affect the behavior of such models. We compare three different methods on attention supervision and their impact on the performances of a state-of-the-art visually grounded language model on two popular vision-and-language tasks.

2020

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Constructing a Public Meeting Corpus
Koji Tanaka | Chenhui Chu | Haolin Ren | Benjamin Renoust | Yuta Nakashima | Noriko Takemura | Hajime Nagahara | Takao Fujikawa
Proceedings of the Twelfth Language Resources and Evaluation Conference

In this paper, we propose a full pipeline of analysis of a large corpus about a century of public meeting in historical Australian news papers, from construction to visual exploration. The corpus construction method is based on image processing and OCR. We digitize and transcribe texts of the specific topic of public meeting. Experiments show that our proposed method achieves a F-score of 87.8% for corpus construction. As a result, we built a content search tool for temporal and semantic content analysis.

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IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
Sora Ohashi | Tomoyuki Kajiwara | Chenhui Chu | Noriko Takemura | Yuta Nakashima | Hajime Nagahara
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

We introduce the IDSOU submission for the WNUT-2020 task 2: identification of informative COVID-19 English Tweets. Our system is an ensemble of pre-trained language models such as BERT. We ranked 16th in the F1 score.

2018

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iParaphrasing: Extracting Visually Grounded Paraphrases via an Image
Chenhui Chu | Mayu Otani | Yuta Nakashima
Proceedings of the 27th International Conference on Computational Linguistics

A paraphrase is a restatement of the meaning of a text in other words. Paraphrases have been studied to enhance the performance of many natural language processing tasks. In this paper, we propose a novel task iParaphrasing to extract visually grounded paraphrases (VGPs), which are different phrasal expressions describing the same visual concept in an image. These extracted VGPs have the potential to improve language and image multimodal tasks such as visual question answering and image captioning. How to model the similarity between VGPs is the key of iParaphrasing. We apply various existing methods as well as propose a novel neural network-based method with image attention, and report the results of the first attempt toward iParaphrasing.