Papers by Toshiyuki Sekiya
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER (2022.acl-long)
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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. |
| Approach: | They propose a demonstration-based learning method which lets the input be prefaced by task demonstrations for in-context learning. |
| Outcome: | The proposed method improves on in-domain learning and domain adaptation in low-resource settings. |
Remedy-R: Generative Reasoning for Machine Translation Evaluation without Error Annotations (2026.findings-acl)
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| Challenge: | Recent MT metrics like xCOMET, Met-ricX, and Remedy have strong correlations with human preferences, but they are black boxes, revealing little insight into why a translation is good or bad. |
| Approach: | They propose a reasoning-driven generative MT metric trained with reinforcement learning from pairwise translation preferences without requiring error-span annotations or distillation from closed LLMs. |
| Outcome: | The proposed reasoning-driven generative MT metric produces step-by-step analyses of accuracy, fluency, and completeness, enabling more interpretable assessments. |
Exploring Context Strategies in LLMs for Discourse-Aware Machine Translation (2025.findings-emnlp)
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| Challenge: | Large language models excel at machine translation, but the impact of how LLMs utilize different forms of contextual information on discourse-level phenomena remains underexplored. |
| Approach: | They examine how different forms of context influence standard MT metrics and specific discourse phenomena such as formality, pronoun selection, and lexical cohesion. |
| Outcome: | Evaluating multiple LLMs across multiple domains and language pairs, the findings consistently show that context boosts translation and discourse-specific performance. |
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models (2023.acl-demo)
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Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Existing models are susceptible to learning spurious biases that do not reflect the underlying task. |
| Approach: | They propose an open-source framework for explanation-based model debugging that allows users to provide various forms of feedback on model explanations. |
| Outcome: | The proposed framework improves model’s OOD performance on text classification tasks by up to 18%. |