Papers by Tomoya Iwakura
Transformer-based Approach for Predicting Chemical Compound Structures (2020.aacl-main)
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| Challenge: | Existing methods to predict chemical compound structures from their names are limited and use handcrafted rules. |
| Approach: | They propose a Transformer-based model that predicts SMILES strings from chemical compound names instead of handcrafted rules. |
| Outcome: | The proposed model achieves higher F-measures than the existing model and the existing one. |
Detecting Heavy Rain Disaster from Social and Physical Sensor (C18-2)
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| Challenge: | Our system detects heavy rain disaster using social and physical sensors. |
| Approach: | They propose a system that detects heavy rain disaster by analyzing tweets and physical sensors. |
| Outcome: | The proposed system detects heavy rain disaster using social and physical sensors in Japan. |
SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization (2025.coling-main)
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| Challenge: | Existing methods to reduce the adverse effect of annotation errors are time-consuming because they require many trained models to detect errors. |
| Approach: | They propose a method that uses a tokenization technique called subword regularization to simulate multiple error detection models for detecting errors. |
| Outcome: | The proposed method performs weighting weighting four to five times faster than existing methods and improves in document classification and named entity recognition tasks. |
On the (In)Effectiveness of Images for Text Classification (2021.eacl-main)
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| Challenge: | Existing studies have focused on text classification, but have shown that images do not improve NLP tasks. |
| Approach: | They focus on text classification, where images complement the text and the Wikipedia page can be in one of a number of different languages. |
| Outcome: | The proposed model trains without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing. |
VLURes: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models (2026.findings-acl)
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| Challenge: | ***VLURes** provides a practical testbed for long-text grounding and multilingual robustness in web-realistic agent settings. |
| Approach: | They propose a multilingual benchmark for evaluating vision-language models under long-text grounding. |
| Outcome: | ***VLURes** provides a testbed for long-text grounding and multilingual robustness in web-realistic agent settings. |
Investigating Neurons and Heads in Transformer-based LLMs for Typographical Errors (2025.emnlp-main)
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| Challenge: | Existing studies have focused on surface-level display of performance degradation due to typos. |
| Approach: | They propose a method to identify typo neurons and typo heads that work actively when inputs contain typos. |
| Outcome: | The proposed method identifies typo neurons and typo heads that work actively when inputs contain typos. |
Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing (D19-1)
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| Challenge: | Named entity recognition (NER) is one of the important basic technologies for Natural Language Processing (NLP) . |
| Approach: | They propose to use long short-term memory (LSTM) of NER model to capture chemical com- pound paraphrases by sharing parameters of LSTM and character embeddings be- tween the two models. |
| Outcome: | The proposed method improves chemi- cal NER and achieves state-of-the-art performance on the BioCreative IV’s CHEMDNER task. |
Global Optimization under Length Constraint for Neural Text Summarization (P19-1)
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| Challenge: | GOLC increases the probabilities of generating summaries that have high evaluation scores within a desired length. |
| Approach: | They propose a global optimization method under length constraint for neural text summarization models. |
| Outcome: | The proposed method generates fewer overlength summaries while maintaining the fastest processing speed. |
Chemical Compounds Knowledge Visualization with Natural Language Processing and Linked Data (L18-1)
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| Challenge: | Existing systems for chemical compounds extraction and registration depend on human labor . CAS databases are being created, but information written in other languages is not exploited well . |
| Approach: | They propose a visualization system for chemical compounds extracted from Japanese texts and chemical compound databases represented as Linked Data (LD) system integrates extracted results with existing chemical compound knowledge to provide different views of chemical compounds. |
| Outcome: | The proposed system integrates extraction results with existing chemical compound knowledge to provide different views of chemical compounds. |