Papers by Shuichiro Shimizu

5 papers
When Large Language Models Meet Speech: A Survey on Integration Approaches (2025.findings-acl)

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Challenge: Recent advances in large language models have spurred interest in expanding their application beyond text-based tasks.
Approach: They propose to categorize the integration of speech with LLMs into three main approaches . they demonstrate how these methods are applied across various speech-related applications .
Outcome: The proposed methods are applied across speech-related applications and highlight the challenges in this field to offer inspiration for future research.
VISA: An Ambiguous Subtitles Dataset for Visual Scene-aware Machine Translation (2022.lrec-1)

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Challenge: Existing multimodal machine translation datasets contain images and video captions or general subtitles which rarely contain linguistic ambiguity.
Approach: They propose a dataset that consists of Japanese-English parallel sentence pairs and corresponding video clips.
Outcome: The proposed dataset is challenging for the latest MMT system and can facilitate MMT research.
MELD-ST: An Emotion-aware Speech Translation Dataset (2024.findings-acl)

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Challenge: Emotion plays a crucial role in human conversation.
Approach: They present a MELD-ST dataset for the emotion-aware speech translation task . they show that fine-tuning with emotion labels can enhance translation performance .
Outcome: The proposed dataset shows that fine tuning with emotion labels can improve translation performance in some settings.
Video-Helpful Multimodal Machine Translation (2023.emnlp-main)

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Challenge: Existing multimodal machine translation datasets contain images and video captions or instructional video subtitles, which rarely contain linguistic ambiguity.
Approach: They propose an MMT dataset that contains ambiguous subtitles and a video-helpful evaluation set.
Outcome: The proposed model performs significantly better than existing models on ambiguous subtitles dataset . it is based on a training set and video-helpful evaluation set .
Towards Speech Dialogue Translation Mediating Speakers of Different Languages (2023.findings-acl)

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Challenge: a new task is proposed to mediate speakers of different languages using speech dialogue translation . we consider context as an important aspect that needs to be addressed in this task . speech translation (ST) has also recently shown success in monologue translation - but no study has focused on ST of dialogues .
Approach: They propose a task to mediate speakers of different languages using speech dialogue translation . they construct a speechBSD dataset and conduct baseline experiments .
Outcome: The proposed task mediates speakers of different languages using speech dialogue translation dataset . it shows that bilingual context performs better in our settings .

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