12 citations · 38 across the 16 of their papers we have counts for
16 papers
Dynamic Injection of Entity Knowledge into Dense Retrievers
Ikuya Yamada, Ryokan Ri, Takeshi Kojima +2
Dense retrievers often struggle with queries involving less-frequent entities due to their limited entity knowledge. We propose the Knowledgeable Passage Retriever (KPR), a BERT-ba…
Natural Fingerprints of Large Language Models
Teppei Suzuki, Ryokan Ri, Sho Takase
Recent studies have shown that the outputs from large language models (LLMs) can often reveal the identity of their source model. While this is a natural consequence of LLMs modeli…
Self-Preference Bias in LLM-as-a-Judge
Koki Wataoka, Tsubasa Takahashi, Ryokan Ri
Automated evaluation leveraging large language models (LLMs), commonly referred to as LLM evaluators or LLM-as-a-judge, has been widely used in measuring the performance of dialogu…
Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability
Ryokan Ri, Shun Kiyono, Sho Takase
Zero-shot cross-lingual transfer by fine-tuning multilingual pretrained models shows promise for low-resource languages, but often suffers from misalignment of internal representat…
Large Vocabulary Size Improves Large Language Models
Sho Takase, Ryokan Ri, Shun Kiyono +1
This paper empirically investigates the relationship between subword vocabulary size and the performance of large language models (LLMs) to provide insights on how to define the vo…
LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation
Ikuya Yamada, Ryokan Ri
Adapting English-based large language models (LLMs) to other languages has become increasingly popular due to the efficiency and potential of cross-lingual transfer. However, exist…
Emergent Communication with Attention
Ryokan Ri, Ryo Ueda, Jason Naradowsky
To develop computational agents that better communicate using their own emergent language, we endow the agents with an ability to focus their attention on particular concepts in th…
EASE: Entity-Aware Contrastive Learning of Sentence Embedding
Sosuke Nishikawa, Ryokan Ri, Ikuya Yamada +2
We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities. The advantage of using entity supervision is…
Pretraining with Artificial Language: Studying Transferable Knowledge in Language Models
Ryokan Ri, Yoshimasa Tsuruoka
We investigate what kind of structural knowledge learned in neural network encoders is transferable to processing natural language. We design artificial languages with structural p…
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models
Ryokan Ri, Ikuya Yamada, Yoshimasa Tsuruoka
Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities. However, existi…
Modeling Target-side Inflection in Placeholder Translation
Ryokan Ri, Toshiaki Nakazawa, Yoshimasa Tsuruoka
Placeholder translation systems enable the users to specify how a specific phrase is translated in the output sentence. The system is trained to output special placeholder tokens,…
Zero-pronoun Data Augmentation for Japanese-to-English Translation
Ryokan Ri, Toshiaki Nakazawa, Yoshimasa Tsuruoka
For Japanese-to-English translation, zero pronouns in Japanese pose a challenge, since the model needs to infer and produce the corresponding pronoun in the target side of the Engl…
Document-aligned Japanese-English Conversation Parallel Corpus
Matīss Rikters, Ryokan Ri, Tong Li +1
Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with l…
Designing the Business Conversation Corpus
Matīss Rikters, Ryokan Ri, Tong Li +1
While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based trainin…
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings
Sosuke Nishikawa, Ryokan Ri, Yoshimasa Tsuruoka
Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingua…
Revisiting the Context Window for Cross-lingual Word Embeddings
Ryokan Ri, Yoshimasa Tsuruoka
Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embedding spaces are structurally similar. The structures…