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Articles by Yoshitomo Matsubara
Personal thoughts on a randomized study of LM-based review feedback agent at ICLR 2025
Last year, ICLR 2025 announced an interesting experiment “Assisting ICLR 2025 reviewers with feedback”. Unfortunately, I had to decline the reviewer invitation from ICLR 2025 for other duties at that time and I missed the opportunity to be part of the interesting experiment. Since then, I had waited for the results of the experiment and feedback from the community as I was super interested in this kind of work. In fact, I proposed a similar idea to organizers of different venues before.
Cross-Lingual Knowledge Distillation for answer sentence selection in low-resource languages
While impressive performance has been achieved on the task of Answer Sentence Selection (AS2) for English, the same does not hold for languages that lack large labeled datasets. In this work, we propose Cross-Lingual Knowledge Distillation (CLKD) from a strong English AS2 teacher as a method to train AS2 models for low-resource languages in the tasks without the need of labeled data for the target language.
TyDi-AS2
TyDi-AS2 and Xtr-TyDi-AS2 are multilingual Answer Sentence Selection (AS2) datasets comprising 8 diverse languages, proposed in our paper accepted at ACL 2023 (Findings): Cross-Lingual Knowledge Distillation for answer sentence selection in low-resource languages. Both the datasets were created from TyDi-QA, a multilingual question-answering dataset. TyDi-AS2 was created by converting the QA instances in TyDi-QA to AS2 instances (see Dataset Creation for details).
Xtr-WikiQA
Xtr-WikiQA is an Answer Sentence Selection (AS2) dataset in 9 non-English languages, proposed in our paper accepted at ACL 2023 (Findings): Cross-Lingual Knowledge Distillation for answer sentence selection in low-resource languages. This dataset is based on an English AS2 dataset, WikiQA (Original, Hugging Face). For translations, we used Amazon Translate. Types Text
[2305.16302] Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages
While impressive performance has been achieved on the task of Answer Sentence Selection (AS2) for English, the same does not hold for languages that lack large labeled datasets. In this work, we propose Cross-Lingual Knowledge Distillation (CLKD) from a strong English AS2 teacher as a method to train AS2 models for low-resource languages in the tasks without the need of labeled data for the target language.
Ensemble transformer for efficient and accurate ranking tasks: An application to question answering systems
This is the official CERBERUS model code repository for our long paper in Findings of EMNLP 2022, "Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems". Types Model
Code and datasets
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Ensemble transformer for efficient and accurate ranking tasks: An application to question answering systems
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