Papers by Kai Nakamura
LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | ***LLaST*** is a framework for building high-performance Large Language model based Speech-to-text Translation systems. |
| Approach: | They propose a framework for building high-performance Large Language model based Speech-to-text Translation systems. |
| Outcome: | The proposed model outperforms the CoVoST-2 benchmark and showcases exceptional scaling capabilities powered by LLMs. |
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets focused on multiturn dialogue systems focus on text or table information. |
| Approach: | They propose a dataset that consists of crowdsourced conversations grounded on Wikipedia text and tables. |
| Outcome: | The proposed dataset shows that there is still ample opportunity for improvement in the current state of dialogue systems. |
Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection (2020.lrec-1)
Copied to clipboard
| Challenge: | Prior fake news datasets lack multimodal text and image data, metadata, comment data, and fine-grained classification at the scale and breadth of their datasets. |
| Approach: | They propose to use a multimodal dataset to build a machine learning classification model that uses text and image data to classify fake news. |
| Outcome: | The proposed model is based on a multimodal dataset consisting of over 1 million samples from multiple categories of fake news. |