Papers by Masaki Asada
ProMQA: Question Answering Dataset for Multimodal Procedural Activity Understanding (2025.naacl-long)
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Kimihiro Hasegawa, Wiradee Imrattanatrai, Zhi-Qi Cheng, Masaki Asada, Susan Holm, Yuran Wang, Ken Fukuda, Teruko Mitamura
| Challenge: | Existing studies typically provide traditional, but less practical evaluation testbeds for multimodal systems. |
| Approach: | They propose a novel evaluation dataset, ProMQA, to measure the advancement of systems in application-oriented scenarios. |
| Outcome: | The proposed evaluation dataset reveals a significant gap between human and competitive multimodal models. |
Assessing the Belief Consistency of Large Language Models on the Logical Conversation Process (2026.acl-long)
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Tomoki Tsujimura, Matīss Rikters, Masaki Asada, Shusaku Egami, Tatsuya Ishigaki, Ken Yano, Hiroya Takamura
| Challenge: | Large language models have been shown remarkable ability to understand given contexts. |
| Approach: | They propose a method to evaluate whether beliefs held by LLMs remain consistent . they propose to use multiple choice question answering format to assess belief consistency . |
| Outcome: | The proposed method evaluates the consistency of LLMs in a multiple-choice question answering format. |
Principled Self-Correction in Discrete Diffusion: A UCB-Guided Framework for Text Generation (2026.eacl-long)
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| Challenge: | Existing diffusion models are trained on corrupted ground-truth tokens, but at inference time they must denoise inputs corruptes from their own predictions. |
| Approach: | They propose a framework that denoises inputs corrupted from their own predictions at inference time. |
| Outcome: | The proposed framework achieves higher faithfulness and coherence over existing diffusion baselines. |
Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training (2025.coling-main)
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| Challenge: | Existing studies show that dependency information is used only for encoder-only-based relation extraction tasks. |
| Approach: | They propose a syntax-aware seq2seq pre-trained model for relation extraction that incorporates dependency information into a seq2-trained language model by continual pre-training with a dependency parsing task. |
| Outcome: | The proposed model incorporates dependency information into a seq2seq pre-trained language model by continual pre-training with a generative sequence-to-sequence (sequ2sq)-based dependency parsing task. |
Addressing the Training-Inference Discrepancy in Discrete Diffusion for Text Generation (2025.coling-main)
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| Challenge: | Existing discrete diffusion models for text generation have a discrepancy between training and inference. |
| Approach: | They propose a training schema that considers two-step diffusion processes and a scheduling technique that gradually increases the probability of using self-generated text as training progresses. |
| Outcome: | The proposed training schema and scheduling technique improve diffusion models on four widely used datasets. |
ELAINE-medLLM: Lightweight English Japanese Chinese Trilingual Large Language Model for Bio-medical Domain (2025.coling-main)
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Ken Yano, Zheheng Luo, Jimin Huang, Qianqian Xie, Masaki Asada, Chenhan Yuan, Kailai Yang, Makoto Miwa, Sophia Ananiadou, Jun’ichi Tsujii
| Challenge: | Existing bilingual or multilingual medical LLMs are limited in multilingual data and therefore perform poorly in non-English languages such as Japanese and Chinese. |
| Approach: | They propose to use a trilingual (English, Japanese, Chinese) large language model adapted for the bio-medical domain to harness the knowledge and abilities of the base model. |
| Outcome: | The proposed model can support English, Japanese, and Chinese and is adapted for a bio-medical domain. |
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information (P18-2)
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| Challenge: | Graph Convolutional Networks (GCNs) can extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. |
| Approach: | They propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. |
| Outcome: | The proposed model can extract drug-drug interactions (DDIs) from texts with high accuracy and the molecular information can enhance text-based extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set. |