13 citations · 26 across the 13 of their papers we have counts for
14 papers
Rubric Spans are Label Representations: Joint LLM Encoding for Short Answer Scoring
Zhifan Sun, Sebastian Gombert, Fabian Zehner +3
Automatic Short Answer Scoring (ASAS) requires models that can score student responses against question-specific criteria while remaining efficient and transferable across rubric s…
Does Translation-Enhanced Speech Encoder Pre-training Affect Speech LLMs?
Tomoya Mizumoto, Yusuke Fujita
Connecting a pre-trained speech encoder to a Large Language Model (LLM) is the standard architecture for building Speech LLMs. However, a structural misalignment exists between the…
Evaluating Japanese Dialect Robustness Across Speech and Text-based Large Language Models
Tomoya Mizumoto, Yusuke Fujita, Hao Shi +3
Dialogue systems based on large language models (LLMs) have advanced significantly in recent years. However, dialectal variation remains a major challenge, particularly for systems…
Serialized Output Prompting for Large Language Model-based Multi-Talker Speech Recognition
Hao Shi, Yusuke Fujita, Tomoya Mizumoto +3
Prompts are crucial for task definition and for improving the performance of large language models (LLM)-based systems. However, existing LLM-based multi-talker (MT) automatic spee…
AC/DC: LLM-based Audio Comprehension via Dialogue Continuation
Yusuke Fujita, Tomoya Mizumoto, Atsushi Kojima +2
We propose an instruction-following audio comprehension model that leverages the dialogue continuation ability of large language models (LLMs). Instead of directly generating targe…
OWSM-Biasing: Contextualizing Open Whisper-Style Speech Models for Automatic Speech Recognition with Dynamic Vocabulary
Yui Sudo, Yusuke Fujita, Atsushi Kojima +2
Speech foundation models (SFMs), such as Open Whisper-Style Speech Models (OWSM), are trained on massive datasets to achieve accurate automatic speech recognition. However, even SF…
Reducing the Cost: Cross-Prompt Pre-Finetuning for Short Answer Scoring
Hiroaki Funayama, Yuya Asazuma, Yuichiroh Matsubayashi +2
Automated Short Answer Scoring (SAS) is the task of automatically scoring a given input to a prompt based on rubrics and reference answers. Although SAS is useful in real-world app…
Developing Interactive Tourism Planning: A Dialogue Robot System Powered by a Large Language Model
Katsumasa Yoshikawa, Takato Yamazaki, Masaya Ohagi +2
In recent years, large language models (LLMs) have rapidly proliferated and have been utilized in various tasks, including research in dialogue systems. We aimed to construct a sys…
Dialogue Systems Can Generate Appropriate Responses without the Use of Question Marks? -- Investigation of the Effects of Question Marks on Dialogue Systems
Tomoya Mizumoto, Takato Yamazaki, Katsumasa Yoshikawa +3
When individuals engage in spoken discourse, various phenomena can be observed that differ from those that are apparent in text-based conversation. While written communication comm…
Tourist Guidance Robot Based on HyperCLOVA
Takato Yamazaki, Katsumasa Yoshikawa, Toshiki Kawamoto +5
This paper describes our system submitted to Dialogue Robot Competition 2022. Our proposed system is a combined model of rule-based and generation-based dialog systems. The system…
Balancing Cost and Quality: An Exploration of Human-in-the-loop Frameworks for Automated Short Answer Scoring
Hiroaki Funayama, Tasuku Sato, Yuichiroh Matsubayashi +3
Short answer scoring (SAS) is the task of grading short text written by a learner. In recent years, deep-learning-based approaches have substantially improved the performance of SA…
Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond
Masato Mita, Keisuke Sakaguchi, Masato Hagiwara +3
Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the…
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
Shun Kiyono, Jun Suzuki, Masato Mita +2
The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, conse…
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?
Masato Mita, Tomoya Mizumoto, Masahiro Kaneko +2
This study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models. GEC models have been previously evaluated based on a single…