Yohei Seki


2024

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Text360Nav: 360-Degree Image Captioning Dataset for Urban Pedestrians Navigation
Chieko Nishimura | Shuhei Kurita | Yohei Seki
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Text feedback from urban scenes is a crucial tool for pedestrians to understand surroundings, obstacles, and safe pathways. However, existing image captioning datasets often concentrate on the overall image description and lack detailed scene descriptions, overlooking features for pedestrians walking on urban streets. We developed a new dataset to assist pedestrians in urban scenes using 360-degree camera images. Through our dataset of Text360Nav, we aim to provide textual feedback from machinery visual perception such as 360-degree cameras to visually impaired individuals and distracted pedestrians navigating urban streets, including those engrossed in their smartphones while walking. In experiments, we combined our dataset with multimodal generative models and observed that models trained with our dataset can generate textual descriptions focusing on street objects and obstacles that are meaningful in urban scenes in both quantitative and qualitative analyses, thus supporting the effectiveness of our dataset for urban pedestrian navigation.

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Multi-Lingual ESG Impact Duration Inference
Chung-Chi Chen | Yu-Min Tseng | Juyeon Kang | Anais Lhuissier | Yohei Seki | Hanwool Lee | Min-Yuh Day | Teng-Tsai Tu | Hsin-Hsi Chen
Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing

To accurately assess the dynamic impact of a company’s activities on its Environmental, Social, and Governance (ESG) scores, we have initiated a series of shared tasks, named ML-ESG. These tasks adhere to the MSCI guidelines for annotating news articles across various languages. This paper details the third iteration of our series, ML-ESG-3, with a focus on impact duration inference—a task that poses significant challenges in estimating the enduring influence of events, even for human analysts. In ML-ESG-3, we provide datasets in five languages (Chinese, English, French, Korean, and Japanese) and share insights from our experience in compiling such subjective datasets. Additionally, this paper reviews the methodologies proposed by ML-ESG-3 participants and offers a comparative analysis of the models’ performances. Concluding the paper, we introduce the concept for the forthcoming series of shared tasks, namely multi-lingual ESG promise verification, and discuss its potential contributions to the field.

2023

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Textual Evidence Extraction for ESG Scores
Naoki Kannan | Yohei Seki
Proceedings of the Fifth Workshop on Financial Technology and Natural Language Processing and the Second Multimodal AI For Financial Forecasting

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Multi-Lingual ESG Impact Type Identification
Chung-Chi Chen | Yu-Min Tseng | Juyeon Kang | Anaïs Lhuissier | Yohei Seki | Min-Yuh Day | Teng-Tsai Tu | Hsin-Hsi Chen
Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing

Assessing a company’s sustainable development goes beyond just financial metrics; the inclusion of environmental, social, and governance (ESG) factors is becoming increasingly vital. The ML-ESG shared task series seeks to pioneer discussions on news-driven ESG ratings, drawing inspiration from the MSCI ESG rating guidelines. In its second edition, ML-ESG-2 emphasizes impact type identification, offering datasets in four languages: Chinese, English, French, and Japanese. Of the 28 teams registered, 8 participated in the official evaluation. This paper presents a comprehensive overview of ML-ESG-2, detailing the dataset specifics and summarizing the performance outcomes of the participating teams.

2002

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Machine Translation Based on NLG from XML-DB
Yohei Seki | Ken’ichi Harada
COLING 2002: The 17th International Conference on Computational Linguistics: Project Notes